Aggregation Mechanism Research Articles

Mechanical force of uterine occupation enables large vesicle extrusion from proteostressed maternal neurons.

Large vesicle extrusion from neurons may contribute to spreading pathogenic protein aggregates and promoting inflammatory responses, two mechanisms leading to neurodegenerative disease. Factors that regulate the extrusion of large vesicles, such as exophers produced by proteostressed C. elegans touch neurons, are poorly understood. Here, we document that mechanical force can significantly potentiate exopher extrusion from proteostressed neurons. Exopher production from the C. elegans ALMR neuron peaks at adult day 2 or 3, coinciding with the C. elegans reproductive peak. Genetic disruption of C. elegans germline, sperm, oocytes, or egg/early embryo production can strongly suppress exopher extrusion from the ALMR neurons during the peak period. Conversely, restoring egg production at the late reproductive phase through mating with males or inducing egg retention via genetic interventions that block egg-laying can strongly increase ALMR exopher production. Overall, genetic interventions that promote ALMR exopher production are associated with expanded uterus lengths and genetic interventions that suppress ALMR exopher production are associated with shorter uterus lengths. In addition to the impact of fertilized eggs, ALMR exopher production can be enhanced by filling the uterus with oocytes, dead eggs, or even fluid, supporting that distention consequences, rather than the presence of fertilized eggs, constitute the exopher-inducing stimulus. We conclude that the mechanical force of uterine occupation potentiates exopher extrusion from proximal proteostressed maternal neurons. Our observations draw attention to the potential importance of mechanical signaling in extracellular vesicle production and in aggregate spreading mechanisms, making a case for enhanced attention to mechanobiology in neurodegenerative disease.

Mechanical force of uterine occupation enables large vesicle extrusion from proteostressed maternal neurons

Large vesicle extrusion from neurons may contribute to spreading pathogenic protein aggregates and promoting inflammatory responses, two mechanisms leading to neurodegenerative disease. Factors that regulate the extrusion of large vesicles, such as exophers produced by proteostressed C. elegans touch neurons, are poorly understood. Here, we document that mechanical force can significantly potentiate exopher extrusion from proteostressed neurons. Exopher production from the C. elegans ALMR neuron peaks at adult day 2 or 3, coinciding with the C. elegans reproductive peak. Genetic disruption of C. elegans germline, sperm, oocytes, or egg/early embryo production can strongly suppress exopher extrusion from the ALMR neurons during the peak period. Conversely, restoring egg production at the late reproductive phase through mating with males or inducing egg retention via genetic interventions that block egg-laying can strongly increase ALMR exopher production. Overall, genetic interventions that promote ALMR exopher production are associated with expanded uterus lengths and genetic interventions that suppress ALMR exopher production are associated with shorter uterus lengths. In addition to the impact of fertilized eggs, ALMR exopher production can be enhanced by filling the uterus with oocytes, dead eggs, or even fluid, supporting that distention consequences, rather than the presence of fertilized eggs, constitute the exopher-inducing stimulus. We conclude that the mechanical force of uterine occupation potentiates exopher extrusion from proximal proteostressed maternal neurons. Our observations draw attention to the potential importance of mechanical signaling in extracellular vesicle production and in aggregate spreading mechanisms, making a case for enhanced attention to mechanobiology in neurodegenerative disease.

Distinct functional diversity of branched oligosaccharides as chaperones and inhibitory-binding partners of amyloid beta-protein and its aggregates

Aggregation and deposition of amyloid beta-protein 1–42 (Aβ42) in the brain, primarily owing to hydrophobic interactions between Aβ42 chains, is a common pathology in all forms of Alzheimer's disease (AD). Hydrophilic oligosaccharides are widely present in the extracellular matrix and on the cytoplasmic membrane. To determine if oligosaccharides bind to Aβ42 or its aggregates and consequently affect their aggregation and cellular function, this study examined the interaction of typical functional oligosaccharides with Aβ42 or its aggregates. Isomaltooligosaccharides (IMOs), particularly isomaltotriose, panose, and isomaltotetraose, functioned as molecular chaperones for Aβ42 by binding directly to Aβ42, preserving Aβ42's active conformation and cytotrophic activity. Oral IMOs reduced total plasma Aβ level and indirectly caused a slight reduction in the load of Aβ42 spots/plaques in the brain of AD model mice (male). Another branched oligosaccharide, bianntennary core pentasaccharide (BCP), had a relatively high binding specificity for Aβ42 oligomers (Aβ42O) and acted as an antagonistic binding partner for Aβ42O. Free BCP effectively blocked/prevented further assembly of Aβ42O and their toxicity to neural and vascular endothelial cell lines. Since BCP is also a signaling component of membrane targets (glycolipids, glycoproteins or receptors), it seemed that BCP had two opposing effects on the binding of Aβ42O to target cells. This study's findings suggest that these branched oligosaccharides may be potential candidates for blocking or preventing Aβ42 aggregation and Aβ42O cytotoxicity/neurotoxicity, respectively, and that IMO-like or free BCP-like oligosaccharide deficiencies in the brain may be one of the underlying mechanisms for Aβ42 aggregation and Aβ42O cytotoxicity.

Single-Molecule Characterization of Heterogeneous Oligomer Formation during Co-Aggregation of 40- and 42-Residue Amyloid-β.

The two most abundant isoforms of amyloid-β (Aβ) are the 40- (Aβ40) and 42-residue (Aβ42) peptides. Since they coexist and there is a correlation between toxicity and the ratio of the two isoforms, quantitative characterization of their interactions is crucial for understanding the Aβ aggregation mechanism. In this work, we follow the aggregation of individual isoforms in a mixture using single-molecule FRET spectroscopy by labeling Aβ42 and Aβ40 with the donor and acceptor fluorophores, respectively. We found that there are two phases of aggregation. The first phase consists of coaggregation of Aβ42 with a small amount of Aβ40, while the second phase results mostly from aggregation of Aβ40. We also found that the aggregation of Aβ42 is slowed by Aβ40 while the aggregation of Aβ40 is accelerated by Aβ42 in a concentration-dependent manner. The formation of oligomers was monitored by incubating mixtures in a plate reader and performing a single-molecule free-diffusion experiment at several different stages of aggregation. The detailed properties of the oligomers were obtained by maximum likelihood analysis of fluorescence bursts. The FRET efficiency distribution is much broader than that of the Aβ42 oligomers, indicating the diversity in isoform composition of the oligomers. Pulsed interleaved excitation experiments estimate that the fraction of Aβ40 in the co-oligomers in a 1:1 mixture of Aβ42 and Aβ40 varies between 0 and 20%. The detected oligomers were mostly co-oligomers especially at the physiological ratio of Aβ42 and Aβ40 (1:10), suggesting the critical role of Aβ40 in oligomer formation and aggregation.

A Peptide Strategy for Inhibiting Different Protein Aggregation Pathways.

Protein aggregation correlates with many human diseases. Protein aggregates differ in structure and shape. Strategies to develop effective aggregation inhibitors that reach the clinic failed so far. Here, we developed a family of peptides targeting early aggregation stages for both amorphous and fibrillar aggregates of proteins unrelated in sequence and structure. They act on dynamic precursors before mechanistic differentiation takes place. Using peptide arrays, we first identified peptides inhibiting the amorphous aggregation of a molten globular, aggregation-prone mutant of the Axin tumor suppressor. Optimization revealed that the peptides activity did not depend on their sequences but rather on their molecular determinants: a composition of 20-30 % flexible, 30-40 % aliphatic and 20-30 % aromatic residues, a hydrophobicity/hydrophilicity ratio close to 1, and an even distribution of residues of different nature throughout the sequence. The peptides also suppressed fibrillation of Tau, a disordered protein that forms amyloids in Alzheimer's disease, and slowed down that of Huntingtin Exon1, an amyloidogenic protein in Huntington's disease, both entirely unrelated to Axin. Our compounds thus target early stages of different aggregation mechanisms, inhibiting both amorphous and amyloid aggregation. Such cross-mechanistic, multi-targeting aggregation inhibitors may be lead compounds for developing drug candidates against various protein aggregation diseases.

Dynamics of waste proteins in brain tissue: Numerical insights into Alzheimer's risk factors.

Over the past few decades, research has indicated that the buildup of waste proteins, like amyloid-β (Aβ), in the brain's interstitial spaces is linked to neurodegenerative diseases like Alzheimer's, but the details of how such proteins are removed from the brain are not well understood. We have developed a numerical model to investigate the aggregation and clearance mechanisms of Aβ in the interstitial spaces of the brain. The model describes the volume-averaged transport of Aβ in a segment of the brain interstitium modeled as a porous medium, oriented between the perivascular space (fluid-filled channel surrounding a blood vessel) of a penetrating arteriole and that of a venule. Our numerical approach solves N coupled advection-diffusion-aggregation equations that model the production, aggregation, fragmentation, and clearance of N species of Aβ. We simulate N=50 species to investigate the oligomer-size dependence of clearance and aggregation. We introduce a timescale plot that helps predict Aβ buildup for different neurological conditions. We show that a sudden increase in monomer concentration, as occurs in conditions like traumatic brain injury, leads to significant plaque formation, which can qualitatively be predicted using the timescale plot. Our results also indicate that impaired protein clearance (as occurs with aging) and fragmentation are both mechanisms that sustain large intermediate oligomer concentrations. Our results provide novel insight into several known risk factors for Alzheimer's disease and cognitive decline, and we introduce a unique framing of Aβ dynamics as a competition between different timescales associated with production rates, aggregation rates, and clearance conditions.

Aggregation Rules of Short Peptides.

The elucidation of aggregation rules for short peptides (e.g., tetrapeptides and pentapeptides) is crucial for the precise manipulation of aggregation. In this study, we derive comprehensive aggregation rules for tetrapeptides and pentapeptides across the entire sequence space based on the aggregation propensity values predicted by a transformer-based deep learning model. Our analysis focuses on three quantitative aspects. First, we investigate the type and positional effects of amino acids on aggregation, considering both the first- and second-order contributions. By identifying specific amino acids and amino acid pairs that promote or attenuate aggregation, we gain insights into the underlying aggregation mechanisms. Second, we explore the transferability of aggregation propensities between tetrapeptides and pentapeptides, aiming to explore the possibility of enhancing or mitigating aggregation by concatenating or removing specific amino acids at the termini. Finally, we evaluate the aggregation morphologies of over 20,000 tetrapeptides, regarding the morphology distribution and type and positional contributions of each amino acid. This work extends the existing aggregation rules from tripeptide sequences to millions of tetrapeptide and pentapeptide sequences, offering experimentalists an explicit roadmap for fine-tuning the aggregation behavior of short peptides for diverse applications, including hydrogels, emulsions, or pharmaceuticals.

Molecular modeling to elucidate the dynamic interaction process and aggregation mechanism between natural organic matters and nanoplastics

The interactions of nanoplastics (NPs) with natural organic materials (NOMs) dominate the environmental fate of both substances and the organic carbon cycle. Their binding and aggregation mechanisms at the molecular level remain elusive due to the high structural complexity of NOMs and aged NPs. Molecular modeling was used to understand the detailed dynamic interaction mechanism between NOMs and NPs. Advanced humic acid models were used, and three types of NPs, i.e., polyethylene (PE), polyvinyl chloride (PVC), and polystyrene (PS), were investigated. Molecular dynamics (MD) simulations revealed the geometrical change of the spontaneous formation of NOMs-NPs supramolecular assemblies. The results showed that pristine NPs initially tend to aggregate homogeneously due to their hydrophobic nature, and then NOM fragments are bound to the formed NP aggregates mainly by vdW interaction. Homo- and hetero-aggregation between NOMs and aged NPs occur simultaneously through various mechanisms, including intermolecular forces and Ca2+ bridging effect, eventually resulting in a mixture of supramolecular structures. Density functional theory calculations were employed to characterize the surface properties and reactivity of the NP monomers. The molecular polarity indices for unaged PE, PS, and PVC were 3.1, 8.5, and 22.2 ​kcal/mol, respectively, which increased to 43.2, 51.6, and 42.2 ​kcal/mol for aged NPs, respectively, indicating the increase in polarity after aging. The vdW and electrostatic potentials of NP monomers were visualized. These results clarified the fundamental aggregation processes, and mechanisms between NPs and NOMs, providing a complete molecular picture of the interactions of nanoparticles in the natural aquatic environment.

Direct evidence for ultrastructures of the α-synuclein-associated synaptic vesicle pool in presynaptic terminals

SNCA/PARK1 encodes α-synuclein, which is associated with familial Parkinson's disease. Despite its abundance in presynaptic terminals, the aggregation mechanism of α-synuclein and its relationship with Parkinson's disease have not yet been elucidated. Moreover, the ultrastructures of α-synuclein localization sites in neuronal presynaptic terminals remain unclear. Therefore, we herein generated transgenic mice expressing human α-synuclein tagged with mKate2 (hSNCA-mKate2 mice). These mice exhibited normal growth and fertility and had no motor dysfunction relative to their wild-type littermates, even at one year old. α-Synuclein-mKate2 accumulated in presynaptic terminals, particularly between Purkinje cells in the cerebellum and neurons in cerebellar nuclei. α-Synuclein-mKate2 was associated with the presynaptic marker, synaptophysin. In-resin CLEM and immunoelectron or electron microscopy revealed that α-synuclein-mKate2 localized on the surface of synaptic vesicles that were tightly arranged and assembled to form large synaptic pools in the cerebellum with negligible effects on the active zone. These results suggest that α-synuclein-associated ultrastructures in the presynaptic terminals of hSNCA-mKate2 mice reflect the structures of α-synuclein-assembled synaptic vesicle pools, and the size of vesicle pools increased. This transgenic mouse model will be a valuable tool for studying α-synuclein-associated synaptic vesicle pools.

Morphological and Biophysical Study of S100A9 Protein Fibrils by Atomic Force Microscopy Imaging and Nanomechanical Analysis.

Atomic force microscopy (AFM) imaging enables the visualization of protein molecules with high resolution, providing insights into their shape, size, and surface topography. Here, we use AFM to study the aggregation process of protein S100A9 in physiological conditions, in the presence of calcium at a molar ratio 4Ca2+:S100A9. We find that S100A9 readily assembles into a worm-like fibril, with a period dimension along the fibril axis of 11.5 nm. The fibril's chain length extends up to 136 periods after an incubation time of 144 h. At room temperature, the fibril's bending stiffness was found to be 2.95×10-28 Nm2, indicating that the fibrils are relatively flexible. Additionally, the values obtained for the Young's modulus (Ex=6.96×105 Pa and Ey=3.37×105 Pa) are four orders of magnitude lower than those typically reported for canonical amyloid fibrils. Our findings suggest that, under the investigated conditions, a distinct aggregation mechanism may be in place in the presence of calcium. Therefore, the findings reported here could have implications for the field of biomedicine, particularly with regard to Alzheimer's disease.

Computational insights into the aggregation mechanism and amyloidogenic core of aortic amyloid medin polypeptide

Medin amyloid, prevalent in the vessel walls of 97 % of individuals over 50, contributes to arterial stiffening and cerebrovascular dysfunction, yet our understanding of its aggregation mechanism remains limited. Dividing the full-length 50-amino-acid medin peptide into five 10-residue segments, we conducted individual investigations on each segment's self-assembly dynamics via microsecond-timescale atomistic discrete molecular dynamics (DMD) simulations. Our findings showed that medin1–10 and medin11–20 segments predominantly existed as isolated unstructured monomers, unable to form stable oligomers. Medin31–40 exhibited moderate aggregation, forming dynamic β-sheet oligomers with frequent association and dissociation. Conversely, medin21–30 and medin41–50 segments demonstrated significant self-assembly capability, readily forming stable β-sheet-rich oligomers. Residue pairwise contact frequency analysis highlighted the critical roles of residues 22–26 and 43–49 in driving the self-assembly of medin21–30 and medin41–50, acting as the β-sheet core and facilitating β-strand formation in other regions within medin monomers, expecting to extend to oligomers and fibrils. Regions containing residues 22–26 and 43–49, with substantial self-assembly abilities and assistance in β-sheet formation, represent crucial targets for amyloid inhibitor drug design against aortic medial amyloidosis (AMA). In summary, our study not only offers deep insights into the mechanism of medin amyloid formation but also provides crucial theoretical and practical guidance for future treatments of AMA.

Do fungi look like macroparasites? Quantifying the patterns and mechanisms of aggregation for host-fungal parasite relationships.

Most hosts contain few parasites, whereas few hosts contain many. This pattern, known as aggregation, is well-documented in macroparasites where parasite intensity distribution among hosts affects host-parasite dynamics. Infection intensity also drives fungal disease dynamics, but we lack a basic understanding of host-fungal aggregation patterns, how they compare to macroparasites, and if they reflect biological processes. To address these gaps, we characterized aggregation of the fungal pathogen Batrachochytrium dendrobatidis (Bd) in amphibian hosts. Utilizing the slope of Taylor's Power Law, we found Bd intensity distributions were more aggregated than macroparasites, conforming closely to lognormal distributions. We observed that Bd aggregation patterns are strongly correlated with known biological processes operating in amphibian populations, such as epizoological phase-invasion, post-invasion, and enzootic-and intensity-dependent disease mortality. Using intensity-dependent mathematical models, we found evidence of evolution of host resistance based on aggregation shifts in systems persisting with Bd following disease-induced declines. Our results show that Bd aggregation is highly conserved across disparate systems and is distinct from aggregation patterns in macroparasites, and contains signatures of potential biological processes of amphibian-Bd systems. Our work lays a foundation to unite host-fungal dynamics under a common theoretical framework and inform future modeling approaches that may elucidate host-fungus interactions.

SwinShadow: Shifted Window for Ambiguous Adjacent Shadow Detection

Shadow detection is a fundamental and challenging task in many computer vision applications. Intuitively, most shadows come from the occlusion of light by the object itself, resulting in the object and its shadow being contiguous (referred to as the adjacent shadow in this paper). In this case, when the color of the object is similar to that of the shadow, existing methods struggle to achieve accurate detection. To address this problem, we present SwinShadow, a transformer-based architecture that fully utilizes the powerful shifted window mechanism for detecting adjacent shadows. The mechanism operates in two steps. Initially, it applies local self-attention within a single window, enabling the network to focus on local details. Subsequently, it shifts the attention windows to facilitate inter-window attention, enabling the capture of a broader range of adjacent information. These combined steps significantly improve the network's capacity to distinguish shadows from nearby objects. And the whole process can be divided into three parts: encoder, decoder, and feature integration. During encoding, we adopt Swin Transformer to acquire hierarchical features. Then during decoding, for shallow layers, we propose a deep supervision (DS) module to suppress the false positives and boost the representation capability of shadow features for subsequent processing, while for deep layers, we leverage a double attention (DA) module to integrate local and shifted window in one stage to achieve a larger receptive field and enhance the continuity of information. Ultimately, a new multi-level aggregation (MLA) mechanism is applied to fuse the decoded features for mask prediction. Extensive experiments on three shadow detection benchmark datasets, SBU, UCF, and ISTD, demonstrate that our network achieves good performance in terms of balance error rate (BER). The source code and results are now publicly available at https://github.com/harrytea/SwinShadow .

Formation mechanism of giant squid myofibrillar protein aggregates induced by egg white protein during heat treatment

In order to solve the problem of poor performance of giant squid gel, this study proposed a method to induce the aggregation of giant squid protein molecules by exogenous additives to improve its gel performance. The particle size, zeta potential, free sulfhydryl group, carbonyl group and hydrophobic index of protein solution were characterized by laser light scattering, fluorescence spectrum and ultraviolet spectrum, and the effect of egg white protein (EWP) on myofibrillar protein (MP) aggregation was analyzed. In the presence of EWP, the density and Rg/Rh value of protein particles decreased significantly after mixing for 600 min, indicating that the interaction between MP and EWP protein could be enhanced by embedding appropriate amount of EWP in the middle of MP protein. With the increase of the proportion of egg white protein, the blue shift of the maximum absorption wavelength of endogenous fluorescence in the mixed protein solution decreased. In the range of 25–65 °C, the change rates of surface charge, free sulfhydryl group, carbonyl group and surface hydrophobicity decreased, which proved that EWP inhibited the self-aggregation rate of MP molecules during the heating process, and induced its gel by interspersing between MP molecules to improve the formation of three-dimensional network during protein thermal gel process. Finally, the addition of EWP improved the gel properties of giant squid surimi. This study offered new direction for improving gel properties and promotes the development and application of giant squid.

Adaptive and Priority-Based Data Aggregation and Scheduling Model for Wireless Sensor Network

Wireless Sensor Networks (WSN) use sensor nodes placed in potential places to collect sensitive information. These sensor nodes monitor necessary data and send it to the sink node. Sensor nodes have resource constraints, especially energy and power depletion. The majority of sensor and battery power is wasted on redundant data transmission. The redundant data transmission consumes a significant amount of the sensor and battery life, decreasing the total lifespan of the sensor nodes. Data aggregation is an approach that expands the useful lifetime of sensor nodes overall and removes unnecessary data and delays. There are many types of data aggregation techniques, such as centralized, tree-based, in-network, and cluster-based. The available tree-based data aggregation mechanism performs well, but the whole tree may be down due to single-node failure. Due to bottlenecks, node data aggregation suffers from an increased packet failure ratio. Another limitation is that every node aggregates data into slices, which consumes more energy. For this purpose, an adaptive and priority-based data aggregation and scheduling model (APB-DASM) for WSNs is proposed in this paper to address these issues. It is proposed that APB-DASM be used to improve quality of service (QoS) with regard to energy consumption and data transmission. The APB-DASM model aggregates sensor data into cluster heads and divides it into three formats: The first format categorizes the most important data that consists of four slices, and the second format categorizes the important data that consists of three slices. Format three data is represented in two slices, which is normal data. These three types of format data are aggregated on a priority basis, such that the highest priority is given to the first format, i.e., most important data; moderate priority is given to the second format, i.e., important data; and then low priority is given to the normal data. Due to an efficient priority-based data aggregation and scheduling algorithm, our proposed model sends the most important data first, and so on. Theoretical study and simulation research demonstrate that our proposed approach improves the existing tree-based models. By using APB-DASM, significant decreases in energy usage, packet delivery ratio, and overall QoS are achieved, and as a result, the WSNs' lifetime is thus increased. The proposed model is implemented in MATLAB, and the results are compared with existing tree-based models. Simulations comparing our model to the most recent models indicate that it worked effectively, reducing the packet failure ratio and energy usage by 36.8% and 30%, respectively, for CBF-ADA, D-SMART, and WDARS. This article emphasizes how the suggested methodology can be effectively used in the aggregation of coronavirus patient data. It demonstrates how adaptable and applicable our method is in the real world.

MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction

Background:The rise of network pharmacology has led to the widespread use of network-based computational methods in predicting drug target interaction (DTI). However, existing DTI prediction models typically rely on a limited amount of data to extract drug and target features, potentially affecting the comprehensiveness and robustness of features. In addition, although multiple networks are used for DTI prediction, the integration of heterogeneous information often involves simplistic aggregation and attention mechanisms, which may impose certain limitations.Results:MSH-DTI, a deep learning model for predicting drug-target interactions, is proposed in this paper. The model uses self-supervised learning methods to obtain drug and target structure features. A Heterogeneous Interaction-enhanced Feature Fusion Module is designed for multi-graph construction, and the graph convolutional networks are used to extract node features. With the help of an attention mechanism, the model focuses on the important parts of different features for prediction. Experimental results show that the AUROC and AUPR of MSH-DTI are 0.9620 and 0.9605 respectively, outperforming other models on the DTINet dataset.Conclusion:The proposed MSH-DTI is a helpful tool to discover drug-target interactions, which is also validated through case studies in predicting new DTIs.

Point defects in coated diamonds

Twelve coated diamonds from mines in the Siberian craton and the Urals were studied to reconstruct the aggregation mechanisms of nitrogen-related defects in natural unirradiated Ib-IaA diamonds. The diamonds' coats contain the following simple nitrogen and nitrogen-vacancy defects: С (N0), NV−, H2 (NVN−), H3 (NVN0) and A (N=N). The NV− and H2 defects are the most intense in the coats containing C-centres: however, the H2 defect is absent in samples with solely A-centres. The dominance and spatial distribution of negatively charged nitrogen-vacancy defects indicate their key role in the C-to-A nitrogen aggregation process. This aggregation occurred in conjunction with the formation of the N3VH defect. Two previously undescribed systems with zero-phonon lines at 836 nm (1.48 eV) and 525.3 nm (2.36 eV) were identified. The 525.3 nm system is associated with lines at 689, 698 and 904 nm and may be related to Y-centres. The diamonds' coats were formed just before the kimberlitic eruption, which resulted in a sharp temperature decrease and the preservation of rapidly transforming low-aggregated defects in outer zones of the coats.

Electromigration Analysis for Interconnects Using Improved Graph Convolutional Network with Edge Feature Aggregation.

Electromigration (EM) is a critical reliability issue in integrated circuits and is becoming increasingly significant as fabrication technology nodes continue to advance. The analysis of the hydrostatic stress, which is paramount in electromigration studies, typically involves solving complex physical equations (partial differential equations, or PDEs in this case), which is time consuming, inefficient and not practical for full-chip EM analysis. In this paper, a novel approach is proposed, conceptualizing circuit interconnect trees as a graph within a graph neural network framework. Using finite element solution software, ground truth hydrostatic stress values were obtained to construct a dataset of interconnected trees with hydrostatic stress values for each node. An improved Graph Convolutional Network (GCN) augmented with edge feature aggregation and attention mechanism was then trained employing the dataset, yielding a model capable of predicting hydrostatic stress values for nodes in an interconnect tree. The results show that our model demonstrated a 15% improvement in the Root Mean Square Error (RMSE) compared to the original GCN model and improved the solution speed greatly compared to traditional finite element software.

Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network

This paper introduces a novel approach to fault detection in the servo motor bearings of industrial robots within the context of Industry 4.0 prognostics and health management. The proposed solution leverages the innovative feature aggregation network for robotic fault detection in the application of smart factory. Overcoming challenges associated with traditional techniques that include handcrafted features, transfer learning, and deep learning models, the proposed approach offers a hierarchical information aggregation mechanism. The model is customized through hyperparameter tuning, resulting in a streamlined architecture with significantly fewer parameters. This parameter efficiency is notably distinct when compared to off-the-shelf transfer learning models that commonly feature extensive parameter counts in the range of hundreds of thousands or millions. The proposed model subjected to rigorous validation across diverse experimental scenarios that affirm its adaptability and robust performance. The model showcases accuracy in fault detection under both simple and welding motion scenarios, while its generalization capabilities are demonstrated as it successfully predicts health states in welding motion, showcasing versatility and reliability across various operational scenarios.

A Novel Design for Joint Collaborative NOMA Transmission with a Two–Hop Multi–Path UE Aggregation Mechanism

A Novel Design for Joint Collaborative NOMA Transmission with a Two–Hop Multi–Path UE Aggregation Mechanism