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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Proanthocyanidins prevent tau protein aggregation and disintegrate tau filaments

Occurrence of neurofibrillary tangles of the tau protein is a hallmark of tau-related neurodegenerative diseases, i.e. Alzheimer's disease(AD) and frontotemporal dementia. The pathological mechanism underlying AD remains poorly understood, and effective treatments are still unavailable to mitigate the disease.Inhibiting of tau aggregation and disrupting the existing fibrils are key targets in drug discovery towards preventing or curing AD. In this study, grape seed proanthocyanidins(GSPs) was found to effectively inhibit the repeat domain of tau(tau-RD) aggregation and disaggregate tau-RD fibrils in a concentrationdependent manner by inhibiting β-sheet formation of tau-RD. In cells, GSPs relieved cytotoxicity induced by tau-RD aggregates. Molecular dynamics simulations indicated that strong hydrogen bonding,hydrophobic interaction and π-π stacking between GSPs and tau-RD protein were major reasons why GSPs had high inhibitory activity on tau-RD fibrillogenesis. These results provide preliminary data to develop GSPs into medicines, foodstuffs or nutritional supplements for AD patients, suggesting that GSPs could be a candidate molecule in the drug design for AD therapeutics.

Multiple effects of polydopamine nanoparticles on Cu^(2+)-mediated Alzheimer's β-amyloid aggregation

Deposition of β-amyloid protein(Aβ) is the main hallmark of Alzheimer's disease(AD), and it has been well recognized that Cu^(2+)-mediated Aβ aggregation plays a crucial role in AD pathological processes.Cu^(2+)binding to Aβ can promote the production of reactive oxygen species(ROS) through Fenton-like reactions and produce more toxic Aβ-Cu^(2+)species under Cu^(2+)stimulation. Thus, the development of nanomaterials that can inhibit Cu^(2+)-mediated Aβ aggregation and degrade Aβ-Cu^(2+)complexes is considered an effective strategy for the prevention and treatment of AD. In this study, polydopamine nanoparticles(PDA NPs) were prepared and the results reveal that PDA NPs potently inhibit Cu^(2+)-mediated Aβaggregation and effectively reduce the formation of Aβ-Cu^(2+)complexes. In vitro experiments show that PDA NPs efficiently eliminate ROS generation catalyzed by Cu^(2+)or Aβ-Cu^(2+)complexes, thus rescuing cultured cells by reducing intracellular ROS levels. More importantly, PDA NPs can depolymerize Aβ-Cu^(2+)complexes, and the degradation of Aβ-Cu^(2+)complexes is promoted by near-infrared light irradiation due to their high photothermal conversion ability. In vivo studies reveal that PDA NPs significantly reduce the deposition of Aβ plaques in the presence of Cu^(2+)and extend the lifespan of AD nematodes from 11 to 14 d. Thus, the PDA NPs developed herein are multifunctional against Cu^(2+)-mediated Aβ aggregation for the potential prevention and treatment of AD.

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.

Aggregation Operators for Decision Making Based on q-Rung Orthopair Fuzzy Hypersoft Sets:An Application in Real Estate Project

In this paper,a decision-making problem with a q-rung orthopair fuzzy hypersoft environment is developed,and two operators of ordered weighted average and induced ordered weighted average are developed.Several fundamental features are also derived.The induced ordered weighted average operator is essential in a q-ROFH environment as the induced ordered aggregation operators are special cases of the existing aggregation operators that already exist in q-ROFH environments.The main function of these operators is to help decision-makers gain a complete understanding of uncertain facts.The proposed aggregation operator is applied to a decision-making problem,with the aim of selecting the most promising real estate project for investment.

Superior aggregation, morphology and photovoltaic performance enabled by fine tuning of fused electron-deficient units in polymer donors

Copolymerization of an electron-rich donor(D)unit with an electron-deficient acceptor(A)unit to construct efficient D-π-A-πtype donors is an effective strategy for organic solar cell applications.The electron-deficient unit fusion,endows extendedπ-conjugation plane and insures excellent photoelectronic property,has great advantages to build A moiety and gradually receives considerable attention.In this work,we adopt benzo[2,1-b:3,4-b’]dithiophene and benzopyrazine(BP),benzothiadiazole(BT)and benzoselenadiazole(BS)to cleverly construct a series of fused A units with different electrondeficient ability,and further synthesize three polymer donors PBDP-BP,PBDP-BT,and PBDP-BS,respectively.The relationships between structure and performance were systematically investigated.PBDPBT shows a moderate aggregation behavior in both solution and film,and the highest hole mobility among the three polymers.After blending with Y6,the PBDP-BT:Y6-based film has the strongest absorption,favorable compatibility,superior crystallinity,and uniform phase separation morphology compared with PBDP-BP or PBDP-BS based blend films.Thus,the device based on PBDP-BT:Y6 has the highest and balanced charge mobility,suppressive recombination,reduced energy loss and achieves an outstanding PCE of 15.14%,which is superior to PBDP-BP:Y6(8.55%)and PBDP-BS:Y6(6.85%).These results provide learnable guidelines for future fused electron-deficient unit-based donor design for photovoltaic application.

Turing/Turing-like patterns:Products of random aggregation of spatial components

Turing patterns are typical spatiotemporal ordered structures in various systems driven far from thermodynamic equilibrium.Turing’s reaction-diffusion theory,containing a long-range inhibiting agent and a local catalytic agent,has provided an explanation for the formation of some patterns in nature.Numerical,experimental and theoretical studies about Turing/Turing-like patterns have been generally focused on systems driven far from thermodynamic equilibrium.The local dynamics of these systems are commonly very complex,which brings great difficulties to understanding of formation of patterns.Here,we investigate a type of Turing-like patterns in a near-equilibrium thermodynamic system experimentally and theoretically,and put forward a new formation mechanism and a quantitative method for Turing/Turing-like patterns.Specifically,we observe a type of Turing-like patterns in starch solutions,and study the effect of concentration on the structure of patterns.The experimental results show that,with the increase of concentration,patterns change from spots to inverse spots,and labyrinthine stripe patterns appear in the region of intermediate concentration.We analyze and model the formation mechanism of these patterns observed in experiments,and the simulation results agree with the experimental results.Our conclusion indicates that the random aggregation of spatial components leads to formation of these patterns,and the proportion of spatial components determines the structures.Our findings shed light on the formation mechanism for Turing/Turing-like patterns.

Effect of extracellular polymeric substances on Dolichospermum aggregation during temperature rise

Dolichospermum,a typical model filamentous of cyanobacteria,has the potential to cause severely bloom.Extracellular polymeric substances(EPSs)are considered to influence the aggregation of the algae,and temperature is a significant factor affecting EPSs secretion.However,the mechanism of how EPSs affects the aggregation of Dolichospermum is still unclear because the structure and composition of EPSs are complex.In this study,the effects of EPSs on the aggregation of Dolichospermum during the rise of temperature(7-37℃)were determined.The results showed that the concentration of extracellular polysaccharides and proteins changed significantly with increasing temperature(P<0.01).Firstly,during the increasing temperature,the polysaccharide content of EPSs increased from 20.34 to 54.64 mg/L,and the polysaccharides in the soluble EPS(S-EPS)layer changed significantly.The protein content reached maximum value at 21℃(14.52 mg/L)and varied significantly in S-EPS and loosely bound EPS(LB-EPS).In the EPSs matrix,humus substances and protein were main components of S-EPS and LB-EPS,and protein was the main component of tightly bound EPS(TB-EPS).Secondly,the cell density of Dolichospermum increased during the temperature rise while the aggregation ratio decreased.Moreover,zeta potential and surface thermodynamic analysis of Dolichospermum revealed that the interfacial free energy and electrostatic repulsion increased gradually with increasing temperature,which further reduced the aggregation of Dolichospermum.Finally,principal component analysis(PCA)analysis showed the aggregation of Dolichospermum was directly related to the changes of protein in EPSs(especially S-EPS and LB-EPS)and zeta potential,and polysaccharides in EPSs inhibited the aggregation of Dolichospermum.Based on these results,it was illustrated that the composition and concentration of EPSs affected the cell surface properties of Dolichospermum with the change of temperature and thus affected the aggregation of Dolichospermum.

Assessing edge-coupled interdependent network disintegration via rank aggregation and elite enumeration

The disintegration of networks is a widely researched topic with significant applications in fields such as counterterrorism and infectious disease control. While the traditional approaches for achieving network disintegration involve identifying critical sets of nodes or edges, limited research has been carried out on edge-based disintegration strategies. We propose a novel algorithm, i.e., a rank aggregation elite enumeration algorithm based on edge-coupled networks(RAEEC),which aims to implement tiling for edge-coupled networks by finding important sets of edges in the network while balancing effectiveness and efficiency. Our algorithm is based on a two-layer edge-coupled network model with one-to-one links, and utilizes three advanced edge importance metrics to rank the edges separately. A comprehensive ranking of edges is obtained using a rank aggregation approach proposed in this study. The top few edges from the ranking set obtained by RAEEC are then used to generate an enumeration set, which is continuously iteratively updated to identify the set of elite attack edges.We conduct extensive experiments on synthetic networks to evaluate the performance of our proposed method, and the results indicate that RAEEC achieves a satisfactory balance between efficiency and effectiveness. Our approach represents a significant contribution to the field of network disintegration, particularly for edge-based strategies.

Dissolvable polymeric microneedles loaded with aspirin for antiplatelet aggregation

To reduce mucosal damage in the gastrointestinal tract caused by aspirin,we developed a dissolvable polymeric microneedle(MN)patch loaded with aspirin.Biodegradable polymers provide mechanical strength to the MNs.The MN tips punctured the cuticle of the skin and dissolved when in contact with the subcutaneous tissue.The aspirin in the MN patch is delivered continuously through an array of micropores created by the punctures,providing a stable plasma concentration of aspirin.The factors affecting the stability of aspirin during MNs fabrication were comprehensively analyzed,and the hydrolysis rate of aspirin in the MNs was less than 2%.Compared to oral administration,MN administration not only had a smoother plasma concentration curve but also resulted in a lower effective dose of antiplatelet aggregation.Aspirin-loaded MNs were mildly irritating to the skin,causing only slight erythema on the skin and recovery within 24 h.In summary,aspirin-loaded MNs provide a new method to reduce gastrointestinal adverse effects in patients requiring aspirin regularly.