Aggregation Kinetics Research Articles

Potassium Chloride as an Effective Alternative to Sodium Chloride in Delaying the Thermal Aggregation of Liquid Whole Egg.

The potential of potassium chloride (KCl) to be used as a substitute for sodium chloride (NaCl) was studied by monitoring the effects of salt treatment on thermal behavior, aggregation kinetics, rheological properties, and protein conformational changes. The results show that the addition of KCl can improve solubility, reduce turbidity and particle size, and positively influence rheological parameters such as apparent viscosity, consistency coefficient (K value), and fluidity index (n). These changes indicate delayed thermal denaturation. In addition, KCl decreased the content of β-sheet and random coil structures and increased the content of α-helix and β-turn structures. The optimal results were obtained with 2% KCl addition, leading to an increase in Tp up to 85.09 °C. The correlation results showed that Tp was positively correlated with solubility, α-helix and β-turn but negatively correlated with ΔH, turbidity, β-sheet and random coil. Overall, compared to NaCl, 2% KCl is more effective in delaying the thermal aggregation of LWE, and these findings lay a solid theoretical foundation for the study of sodium substitutes in heat-resistant liquid egg products.

LC-MS Characterization and Stability Assessment Elucidate Correlation Between Charge Variant Composition and Degradation of Monoclonal Antibody Therapeutics.

Aggregation stability of monoclonal antibody (mAb) therapeutics is influenced by many critical quality attributes (CQA) such as charge and hydrophobic variants in addition to environmental factors. In this study, correlation between charge heterogeneity and stability of mAbs for bevacizumab and trastuzumab has been investigated under a variety of stresses including thermal stress at 40°C, thermal stress at 55°C, shaking (mechanical), and low pH. Size- and charge-based heterogeneities were monitored using analytical size exclusion chromatography(SEC) and cation exchange chromatography(CEX), respectively, while dynamic light scattering was used to assess changes in hydrodynamic size. CEX analysis revealed an increase in cumulative acidic content for all variants of both mAbs post-stress treatment attributed to increased deamidation. Higher charge heterogeneity was observed in variants eluting close to the main peak than the ones eluting further away (25-fold and 42-fold increase in acidic content for main and B1 of bevacizumab and 19-fold for main of trastuzumab, respectively, under thermal stress; 50-fold increase in acidic for main and B1 of bevacizumab and 10% rise in basic content of main of trastuzumab under pH stress). Conversely, variants eluting far away from main exhibit greater aggregation as compared to close-eluting ones. Aggregation kinetics of variants followed different order for the different stresses for both mAbs (2nd order for thermal and pH stresses and 0th order for shaking stress). Half-life of terminal charge variants of both mAbs was 2- to 8-fold less than main indicating increased degradation propensity.

Colorimetric Sensor Array for Simultaneous Assay of Catecholamine Neurotransmitters Using Various-Sized Citrate-Capped Gold Nanoparticles

Driven by the need to in situ monitoring of the endogenous substances in biological matrices, a simple, rapid and sensitive optical nanoarray system has been designed for determination and visual discrimination of four important catecholamine neurotransmitters: dopamine (DA), epinephrine (EP), norepinephrine (NE) and levodopa (LD). To fabricate this platform, spherical citrate-capped gold nanoparticles (AuNPs) in the size range of 13–42 nm have been employed as sensing elements. For each catecholamine different responses toward the AuNPs was obtained indicating unique aggregation kinetics. When the sensor array exposed to different catecholamines (CAs), both the color and UV–Vis absorbance spectra changed due to the interaction of analytes and AuNPs, creating a unique fingerprint-like pattern for each analyte which can be visually detected by naked eye. To analyze response profiles, we employed unsupervised chemometric algorithms including hierarchical cluster analysis (HCA) and principal component analysis (PCA) and satisfactory class separations were achieved. Furthermore, the sensitive RGB based color difference patterns were generated providing easily monitored visual output. Based on this sensor array, the set of four catecholamine neurotransmitters have been correctly identified and classified in the spiked real urine samples. The target analytes have been well distinguished at low concentrations of 0.3 − 4 μM DA, 0.8 − 10 μM EP, 0.8 − 8 μM NE, and 0.3 − 10 μM LD using the proposed “chemical nose” strategy. We believe that the established optical sensor array can be potentially employed as an efficient discrimination tool for diagnostic and therapeutic applications in the future.

Both nanoplastic and iron mineral types determine their heteroaggregation: Aggregation kinetics and interface process

Nanoplastics (NPs) inevitably interact with iron minerals (IMs) after being released into aquatic environments, changing their transport and fate. In this study, batch heteroaggregation kinetics of four types of NPs, i.e., polymethyl methacrylate (PMMA), polystyrene (PS-Bare), amino-polystyrene (PS-NH2), and carboxyl-polystyrene (PS-COOH), with two different IMs (hematite and magnetite) were conducted. We found that the heteroaggregation of NPs and IMs and the associated interfacial interaction mechanisms are both NPs-dependent and IMs-dependent. Specifically, the NPs had stronger heteroaggregation with hematite than magnetite; the heteroaggregation order of two IMs with NPs was PMMA > PS-NH2 > PS-Bare > PS-COOH. Moreover, hydrogen bond, complexation, hydrophobic, cation-π, and electrostatic interaction were involved in the interfacial reaction between NPs and hematite, and electrons were transferred from the NPs to the hematite, causing the reduction of Fe3+ into Fe2+. Furthermore, we first revealed that both pre-homoaggregation of NPs and IMs could affect their subsequent heteroaggregation, and the homoaggregates of IMs could be interrupted by PMMA or PS-COOH NPs introduction. Therefore, the emerging NPs pollution is likely to generate an ecological effect in terms of elemental cycles such as iron cycle. This work provides new insights into assessing the environmental transfer and ecological effects of NPs in aquatic environments.

Quantitative systems pharmacology-based exploration of relevant anti-amyloid therapy challenges in clinical practice.

Addressing practical challenges in clinical practice after the recent approvals of amyloid antibodies in Alzheimer's disease (AD) will benefit more patients. However, generating these answers using clinical trials or real-world evidence is not practical, nor feasible. Here we use a Quantitative Systems Pharmacology (QSP) computational model of amyloid aggregation dynamics, well validated with clinical data on biomarkers and amyloid-related imaging abnormality-edema (ARIA-E) liability of six amyloid antibodies in clinical trials to explore various clinical practice challenges. Treatment duration to reach amyloid negativity ranges from 12 to 44, 16 to 40, and 6 to 20 months for lecanemab, aducanumab, and donanemab, respectively, for baseline central amyloid values between 50 and 200 Centiloids (CL). Changes in plasma cerebrospinal fluid Aβ42 and the plasma Aβ42/ Aβ40 ratio-fluid biomarkers to detect central amyloid negativity-is greater for lecanemab than for aducanumab and donanemab, indicating that these fluid amyloid biomarkers are only suitable for lecanemab. After reaching amyloid negativity an optimal maintenance schedule consists of a 24-month, 48-month and 64-month interval for 10mg/kg (mpk) lecanemab, 10mpk aducanumab, and 20mpk donanemab, respectively, to keep central amyloid negative for 10 years. Cumulative ARIA-E liability could be reduced to almost half by introducing a drug holiday in the first months. For patients experiencing ARIA-E, restarting treatment with a conservative titration strategy resulted in an additional delay ranging between 3 and 4 months (donanemab), 5 months (lecanemab), and up to 7 months (aducanumab) for reaching amyloid negativity, depending upon the timing of the incident. Clinical trial designs for Down syndrome patients suggested the same rank order for central amyloid reduction, but higher ARIA-E liability especially for donanemab, which can be significantly mitigated by adopting a longer titration period. This QSP platform could support clinical practice challenges to optimize real-world treatment paradigms for new and existing amyloid drugs.

Mechanism of ultrasound-induced soybean/egg white composite gelation: Gel properties, morphological structure and co-aggregation kinetics

This study aims to develop ultrasound-assisted acid-induced egg white protein (EWP)-soy protein isolate (SPI) composite gels and to investigate the mechanistic relationship between the co-aggregation behavior of composite proteins and gel properties through aggregation kinetics monitored continuously by turbidity. The results showed that the inclusion of EWP caused the attenuation of gel properties and maximum aggregation (Amax) because EWP could aggregate with SPI at a higher rate (Kapp), which impeded the interaction between SPI and the formation of a continuous gelling network. In the EWP-dominated system, SPI with higher molecular weights also increased the fractal dimension of gels. Ultrasound improved properties of composite gels, especially the SPI-dominated system. After ultrasound treatment, the small, uniform size of co-aggregates and the decrease in potential led to an increase in the aggregation rate and formation of dense particles, consistent with an increase in gelation rate and texture properties. Excessively fast aggregation generated coarse chains and more pores. Still, the exposure of free sulfhydryl groups assisted the gel structure units to form a compact network through disulfide bonding. On the whole, the study could provide theoretical support for a deeper understanding on the interaction mechanism and gelation of composite proteins.

Salvianolic acid B enhances tissue repair and regeneration by regulating immune cell migration and Caveolin-1-mediated blastema formation in zebrafish

IntroductionNon-healing wounds resulting from trauma, surgery, and chronic diseases annually affect millions of individuals globally, with limited therapeutic strategies available due to the incomplete understanding of the molecular processes governing tissue repair and regeneration. Salvianolic acid B (Sal B) has shown promising bioactivities in promoting angiogenesis and inhibiting inflammation. However, its regulatory mechanisms in tissue regeneration remain unclear. PurposeThis study aims to investigate the effects of Sal B on wound healing and regeneration processes, along with its underlying molecular mechanisms, by employing zebrafish as a model organism. MethodsIn this study, we employed a multifaceted approach to evaluate the impact of Sal B on zebrafish tail fin regeneration. We utilized whole-fish immunofluorescence, TUNEL staining, mitochondrial membrane potential (MMP), and Acridine Orange (AO) probes to analyze the tissue repair and regenerative under Sal B treatment. Additionally, we utilized transgenic zebrafish strains to investigate the migration of inflammatory cells during different phases of fin regeneration. To validate the importance of Caveolin-1 (Cav1) in tissue regeneration, we delved into its functional role using molecular docking and Morpholino-based gene knockdown techniques. Additionally, we quantified Cav1 expression levels through the application of in situ hybridization. ResultsOur findings demonstrated that Sal B expedites zebrafish tail fin regeneration through a multifaceted mechanism involving the promotion of cell proliferation, suppression of apoptosis, and enhancement of MMP. Furthermore, Sal B was found to exert regulatory control over the dynamic aggregation and subsequent regression of immune cells during tissue regenerative processes. Importantly, we observed that the knockdown of Cav1 significantly compromised tissue regeneration, leading to an excessive infiltration of immune cells and increased levels of apoptosis. Moreover, the knockdown of Cav1 also affects blastema formation, a critical process influenced by Cav1 in tissue regeneration. ConclusionThe results of this study showed that Sal B facilitated tissue repair and regeneration through regulating of immune cell migration and Cav1-mediated fibroblast activation, promoting blastema formation and development. This study highlighted the potential pharmacological effects of Sal B in promoting tissue regeneration. These findings contributed to the advancement of regenerative medicine research and the development of novel therapeutic approaches for trauma.

Dissecting the effect of ALS mutation S375G on the conformational properties and aggregation dynamics of TDP-43370-375 fragment

The aggregation of transactive response deoxyribonucleic acid (DNA) binding protein of 43 kDa (TDP-43) into ubiquitin-positive inclusions is closely associated with amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration, and chronic traumatic encephalopathy. The 370–375 fragment of TDP-43 (370GNNSYS375, TDP-43370-375), the amyloidogenic hexapeptides, can be prone to forming pathogenic amyloid fibrils with the characteristic of steric zippers. Previous experiments reported the ALS-associated mutation, serine 375 substituted by glycine (S375G) is linked to early onset disease and protein aggregation of TDP-43. Based on this, it is necessary to explore the underlying molecular mechanisms. By utilizing all-atom molecular dynamics (MD) simulations of 102 μs in total, we investigated the impact of S375G mutation on the conformational ensembles and oligomerization dynamics of TDP-43370-375 peptides. Our replica exchange MD simulations show that S375G mutation could promote the unstructured conformation formation and induce peptides to form a loose packed oligomer, thus inhibiting the aggregation of TDP-43370-375. Further analyses suggest that S375G mutation displays a reduction effect on the number of total hydrogen bonds and contacts among TDP-43370-375 peptides. Hydrogen bonding and polar interactions among TDP-43370-375 peptides, as well as Y374-Y374 π-π stacking interaction, are attenuated by S375G mutation. Additional microsecond MD simulations demonstrate that S375G mutation could prohibit the conformational conversion to β-structure-rich aggregates and possess an inhibitory effect on the oligomerization dynamics of TDP-43370-375. This study offers for the first time of molecular insights into the S375G mutation affecting the aggregation of TDP-43370-375 at the atomic level, and may open new avenues in the development of future site-specific mutation therapeutics.

FedDUS: Lung tumor segmentation on CT images through federated semi-supervised with dynamic update strategy

Background and ObjectiveLung tumor annotation is a key upstream task for further diagnosis and prognosis. Although deep learning techniques have promoted automation of lung tumor segmentation, there remain challenges impeding its application in clinical practice, such as a lack of prior annotation for model training and data-sharing among centers. MethodsIn this paper, we use data from six centers to design a novel federated semi-supervised learning (FSSL) framework with dynamic model aggregation and improve segmentation performance for lung tumors. To be specific, we propose a dynamically updated algorithm to deal with model parameter aggregation in FSSL, which takes advantage of both the quality and quantity of client data. Moreover, to increase the accessibility of data in the federated learning (FL) network, we explore the FAIR data principle while the previous federated methods never involve. ResultThe experimental results show that the segmentation performance of our model in six centers is 0.9348, 0.8436, 0.8328, 0.7776, 0.8870 and 0.8460 respectively, which is superior to traditional deep learning methods and recent federated semi-supervised learning methods. ConclusionThe experimental results demonstrate that our method is superior to the existing FSSL methods. In addition, our proposed dynamic update strategy effectively utilizes the quality and quantity information of client data and shows efficiency in lung tumor segmentation. The source code is released on (https://github.com/GDPHMediaLab/FedDUS).

FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update

As a promising approach to deal with distributed data, Federated Learning (FL) achieves major advancements in recent years. FL enables collaborative model training by exploiting the raw data dispersed in multiple edge devices. However, the data is generally non-independent and identically distributed, i.e., statistical heterogeneity, and the edge devices significantly differ in terms of both computation and communication capacity, i.e., system heterogeneity. The statistical heterogeneity leads to severe accuracy degradation while the system heterogeneity significantly prolongs the training process. In order to address the heterogeneity issue, we propose an Asynchronous Staleness-aware Model Update FL framework, i.e., FedASMU, with two novel methods. First, we propose an asynchronous FL system model with a dynamical model aggregation method between updated local models and the global model on the server for superior accuracy and high efficiency. Then, we propose an adaptive local model adjustment method by aggregating the fresh global model with local models on devices to further improve the accuracy. Extensive experimentation with 6 models and 5 public datasets demonstrates that FedASMU significantly outperforms baseline approaches in terms of accuracy (0.60% to 23.90% higher) and efficiency (3.54% to 97.98% faster).

DGA-GNN: Dynamic Grouping Aggregation GNN for Fraud Detection

Fraud detection has increasingly become a prominent research field due to the dramatically increased incidents of fraud. The complex connections involving thousands, or even millions of nodes, present challenges for fraud detection tasks. Many researchers have developed various graph-based methods to detect fraud from these intricate graphs. However, those methods neglect two distinct characteristics of the fraud graph: the non-additivity of certain attributes and the distinguishability of grouped messages from neighbor nodes. This paper introduces the Dynamic Grouping Aggregation Graph Neural Network (DGA-GNN) for fraud detection, which addresses these two characteristics by dynamically grouping attribute value ranges and neighbor nodes. In DGA-GNN, we initially propose the decision tree binning encoding to transform non-additive node attributes into bin vectors. This approach aligns well with the GNN’s aggregation operation and avoids nonsensical feature generation. Furthermore, we devise a feedback dynamic grouping strategy to classify graph nodes into two distinct groups and then employ a hierarchical aggregation. This method extracts more discriminative features for fraud detection tasks. Extensive experiments on five datasets suggest that our proposed method achieves a 3% ~ 16% improvement over existing SOTA methods. Code is available at https://github.com/AtwoodDuan/DGA-GNN.

A Dynamic GCN with Cross-Representation Distillation for Event-Based Learning

Recent advances in event-based research prioritize sparsity and temporal precision. Approaches learning sparse point-based representations through graph CNNs (GCN) become more popular. Yet, these graph techniques hold lower performance than their frame-based counterpart due to two issues: (i) Biased graph structures that don't properly incorporate varied attributes (such as semantics, and spatial and temporal signals) for each vertex, resulting in inaccurate graph representations. (ii) A shortage of robust pretrained models. Here we solve the first problem by proposing a new event-based GCN (EDGCN), with a dynamic aggregation module to integrate all attributes of vertices adaptively. To address the second problem, we introduce a novel learning framework called cross-representation distillation (CRD), which leverages the dense representation of events as a cross-representation auxiliary to provide additional supervision and prior knowledge for the event graph. This frame-to-graph distillation allows us to benefit from the large-scale priors provided by CNNs while still retaining the advantages of graph-based models. Extensive experiments show our model and learning framework are effective and generalize well across multiple vision tasks.

Soil and management effects on aggregation and organic matter dynamics in vineyards

Vineyards in hilly areas are often prone to erosion because of poor soil development, low soil organic matter (SOM) contents, and severe slopes. Permanent grassing, besides providing protection against erosion, can foster the soil resistance to degradation by increasing SOM that, in turn, favours the formation and limits the turnover of stable aggregates by promoting interactions between SOM and soil minerals. Although a soil’s capacity to physically protect SOM is known to depend on management practices, soil properties may interact causing the soil response to vary even at relatively low spatial scales, thereby warranting site-specific sustainable soil management. The objective of this study was to evaluate how differences in soil properties within the same vineyard may modulate the effects of management practices on aggregate formation, stability as well as SOM dynamics. We exploited the spatial differences in soil types and properties in an experimental vineyard by sampling topsoil samples (0–5 cm) with contrasting texture and pH to include a non-calcareous clay loam from an Alfisol (pH 6.8, 18.6 g kg−1 organic C, 28% clay) and a calcareous loam from an Entisol (pH 8.0, 14.8 g kg−1 organic C, 14% clay), both managed for 3 years under permanent grass vs. autumn tillage. Four aggregate size fractions were separated by dry sieving, and characterized for their organic C content and distribution between functional pools by density fractionation, and their resistance to breakdown and organic C loss during wet sieving.Soil type affected aggregate formation and stability with a greater abundance of larger aggregates richer in SOM in the clay loam with respect to the calcareous loam. Tillage enhanced aggregate breakdown and SOM loss, but the effects were highly dependent on the intrinsic soil properties that drive the different mechanisms of aggregate formation and stabilization. The largest macroaggregates were most susceptible to disruption and organic C release by tillage, but a soil-dependent effect was observed as those in the calcareous Entisol were more vulnerable than those in the Alfisol topsoil, particularly due to rapid losses resulting from slaking, swelling and dispersion. Although the aggregates in fine-textured topsoil may be relatively less vulnerable to tillage, the higher proportion of organic C in the larger macroaggregates may make them nonetheless prone to significant organic C loss (in the form of POM (particulate OM) or SOM-rich fine aggregates) unless appropriate management is adopted. Therefore, special attention should be given to the spatial variability in soil properties when planning vineyard management to reduce soil degradation.

Amino acids and glycine derivatives differently affect refolding of mesophilic and thermophilic like α-amylases: implications in protein refolding and aggregation

α-amylases are industrially important enzymes which are used in different starch-based industries. They are adapted to different environmental conditions like extremes of temperature, pH and salinity. Herein, α-amylases from Bacillus amyloliquifaciens (BAA) and Bacillus licheniformis (BLA), representing mesophilic and thermophilic-like proteins, respectively, have been used to investigate the effect of naturally occurring osmolytes like arginine, proline, glycine and its methyl derivatives, sarcosine and betaine on their refolding. In this study, we have shown that among amino acids and glycine derivatives, betaine is the most promising osmolyte, while arginine and glycine exhibit moderately positive effect at their lower concentrations on the refolding of BAA only. Except betaine, all other osmolytes above 0.25 M showed inhibitory effect on the native enzyme activity of BLA and BAA. However, aggregation kinetics monitored by static light scattering indicates suppression of aggregation by all of these osmolytes. Further investigation by tryptophan and ANS fluorescence spectroscopy indicates the formation of compact hydrophobic core in the presence of the osmolytes. The morphology of protein aggregates having different sizes was visualized by atomic force microscopy ,and it was observed that amorphous aggregates of variable heights were formed. Our study highlights the importance of differential effects of arginine, proline, glycine, sarcosine and betaine on the native state as well as on refolding of BLA and BAA which may be helpful in devising strategies for developing effective protein formulation and prevention of aggregation of industrially and therapeutically important proteins. Communicated by Ramaswamy H. Sarma

Structural effects of charge destabilization and amino acid substitutions in amyloid fragments of CsgA

The most studied functional amyloid is the CsgA, major curli subunit protein, which is produced by numerous strains of Enterobacteriaceae. Although CsgA sequences are highly conserved, they exhibit species diversity, which reflects the specific evolutionary and functional adaptability of the major curli subunit. Herein, we performed bioinformatics analyses to uncover the differences in the amyloidogenic properties of the R4 fragments in Escherichia coli and Salmonella enterica and proposed four mutants for more detailed studies: M1, M2, M3, and M4. The mutated sequences were characterized by various experimental techniques, such as circular dichroism, ATR-FTIR, FT-Raman, thioflavin T, transmission electron microscopy and confocal microscopy. Additionally, molecular dynamics simulations were performed to determine the role of buffer ions in the aggregation process. Our results demonstrated that the aggregation kinetics, fibril morphology, and overall structure of the peptide were significantly affected by the positions of charged amino acids within the repeat sequences of CsgA. Notably, substituting glycine with lysine resulted in the formation of distinctive spherically packed globular aggregates. The differences in morphology observed are attributed to the influence of phosphate ions, which disrupt the local electrostatic interaction network of the polypeptide chains. This study provides knowledge on the preferential formation of amyloid fibrils based on charge states within the polypeptide chain.

Modulation of Primary and Secondary Processes in Tau Fibril Formation by Salt-Induced Dynamics.

The initial stages of amyloid fibrilization begin with the monomers populating aggregation-prone conformers. Characterization of such aggregation-prone conformers is crucial in the study of neurodegenerative diseases. The current study characterizes the aggregation pathway of two tau protein constructs that have been recently demonstrated to form Alzheimer's (AD) fibril structures with divalent ions and chronic traumatic encephalopathy (CTE) fibril structures with monovalent ions. The results highlight the involvement of identical residues in both the primary and secondary processes of both AD and CTE fibril propagation. Nuclear magnetic resonance relaxation experiments reveal increased flexibility of the motifs 321KCGS within R3 and 364PGGGN within R4 in the presence of MgCl2/NaCl, correlating with faster aggregation kinetics and indicating efficient primary nucleation. Notably, the seeded aggregation kinetics of the tau monomers in the presence and absence of metal ions are strikingly different. This correlates with the overall sign of the 15N-ΔR2 profile specifying the dominant mechanism involved in the process of aggregation.

Aggregation, retention and transport of γ-MnO2 nanoparticles in water-saturated porous media: Impact on the immobility of thallium

Nano-scale Mn oxides can act as effective stabilizers for Tl in soil and sediments. Nevertheless, the comprehensive analysis of the capacity of MnO2 to immobilize Tl in such porous media has not been systematically explored. Therefore, this study investigates the impact of γ-MnO2, a model functional nanomaterial for remediation, on the mobility of Tl in a water-saturated quartz sand-packed column. The mechanisms involved are further elucidated based on the adsorption and aggregation kinetics of γ-MnO2. The results indicate that higher ionic strength (IS) and the presence of ion Ca(II) promote the aggregation of γ-MnO2, resulting from the reduced electrostatic repulsion between particles. Conversely, an increase in pH inhibits aggregation due to enhanced interaction energy. γ-MnO2 significantly influences Tl retention and mobility, with a substantial fraction of γ-MnO2-bound Tl transported through the column. This might be attributed to the high affinity of γ-MnO2 for Tl through ion exchange reactions and precipitation at the surface of γ-MnO2. The mobility of Tl in the sand column is influenced by the γ-MnO2 colloids, exhibiting either inhibition or promotion depending on the pH, IS, and cation type of the solution. In solutions with higher IS and Ca(II), the mobility of Tl decreases as γ-MnO2 colloids tend to aggregate, strain, and block, facilitating colloidal Tl retention in porous media. Although higher pH reduces the mobility of individual Tl, it promotes the mobility of γ-MnO2 colloids, facilitating a substantial fraction of colloidal-form Tl. Consequently, the optimal conditions for stabilizing Tl by γ-MnO2 involve either high IS and low pH or the presence of competitive cations (e.g., Ca(II)). These findings provide new insights into Tl immobilization using MnO2- and Mn oxide-based functional materials, offering potential applications in the remediation of Tl contamination in soil and groundwater.

Expectation-driven boom-bust cycles

Using data from the Survey of Professional Forecasters, we observe that a large fraction of analysts’ expectations about future economic growth is not due to technology or other shocks to fundamentals measured by the business cycle literature. We find that these unexplained changes in forecast revisions predict significant boom-bust dynamics in the key macroeconomic aggregates. We offer a novel theory where boom-bust dynamics stem from expectation shocks orthogonal to fundamentals.

Dynamic multiple-graph spatial-temporal synchronous aggregation framework for traffic prediction in intelligent transportation systems.

Accurate traffic prediction contributes significantly to the success of intelligent transportation systems (ITS), which enables ITS to rationally deploy road resources and enhance the utilization efficiency of road networks. Improvements in prediction performance are evident by utilizing synchronized rather than stepwise components to model spatial-temporal correlations. Some existing studies have designed graph structures containing spatial and temporal attributes to achieve spatial-temporal synchronous learning. However, two challenges remain due to the intricate dynamics: (a) Accounting for the impact of external factors in spatial-temporal synchronous modeling. (b) Multiple perspectives in constructing spatial-temporal synchronous graphs. To address the mentioned limitations, a novel model named dynamic multiple-graph spatial-temporal synchronous aggregation framework (DMSTSAF) for traffic prediction is proposed. Specifically, DMSTSAF utilizes a feature augmentation module (FAM) to adaptively incorporate traffic data with external factors and generate fused features as inputs to subsequent modules. Moreover, DMSTSAF introduces diverse spatial and temporal graphs according to different spatial-temporal relationships. Based on this, two types of spatial-temporal synchronous graphs and the corresponding synchronous aggregation modules are designed to simultaneously extract hidden features from various aspects. Extensive experiments constructed on four real-world datasets indicate that our model improves by 3.68-8.54% compared to the state-of-the-art baseline.

Convolutional transformer-driven robust electrocardiogram signal denoising framework with adaptive parametric ReLU.

The electrocardiogram (ECG) is a widely used diagnostic tool for cardiovascular diseases. However, ECG recording is often subject to various noises, which can limit its clinical evaluation. To address this issue, we propose a novel Transformer-based convolutional neural network framework with adaptively parametric ReLU (APtrans-CNN) for ECG signal denoising. The proposed APtrans-CNN architecture combines the strengths of transformers in global feature learning and CNNs in local feature learning to address the inadequacy of learning with long sequence time-series features. By fully exploiting the global features of ECG signals, our framework can effectively extract critical information that is necessary for signal denoising. We also introduce an adaptively parametric ReLU that can assign a value to the negative information contained in the ECG signal, thereby overcoming the limitation of ReLU to retain negative information. Additionally, we introduce a dynamic feature aggregation module that enables automatic learning and retention of valuable features while discarding useless noise information. Results obtained from two datasets demonstrate that our proposed APtrans-CNN can accurately extract pure ECG signals from noisy datasets and is adaptable to various applications. Specifically, when the input consists of ECG signals with a signal-to-noise ratio (SNR) of -4 dB, APtrans-CNN successfully increases the SNR to more than 6 dB, resulting in the diagnostic model's accuracy exceeding 96%.