Aggregation Process Research Articles

Development of a New Aggregation Method to Remove Nanoplastics from the Ocean: Proof of Concept Using Mussel Exposure Tests

The overproduction and mismanagement of plastics has led to the accumulation of these materials in the environment, particularly in the marine ecosystem. Once in the environment, plastics break down and can acquire microscopic or even nanoscopic sizes. Given their sizes, microplastics (MPs) and nanoplastics (NPs) are hard to detect and remove from the aquatic environment, eventually interacting with marine organisms. This research mainly aimed to achieve the aggregation of micro- and nanoplastics (MNPs) to ease their removal from the marine environment. To this end, the size and stability of polystyrene (PS) MNPs were measured in synthetic seawater with the different components of the technology (ionic liquid and chitosan). The MPs were purchased in their plain form, while the NPs displayed amines on their surface (PS NP-NH2). The results showed that this technology promoted a significant aggregation of the PS NP-NH2, whereas, for the PS MPs, no conclusive results were found, indicating that the surface charge plays an essential role in the MNP aggregation process. Moreover, to investigate the toxicological potential of MNPs, a mussel species (M. galloprovincialis) was exposed to different concentrations of MPs and NPs, separately, with and without the technology. In this context, mussels were sampled after 7, 14, and 21 days of exposure, and the gills and digestive glands were collected for analysis of oxidative stress biomarkers and histological observations. In general, the results indicate that MNPs trigger the production of reactive oxygen species (ROS) in mussels and induce oxidative stress, making gills the most affected organ. Yet, when the technology was applied in moderate concentrations, NPs showed adverse effects in mussels. The histological analysis showed no evidence of MNPs in the gill’s tissues.

Polymeric Materials as a Promising Platform for Suppressing Protein Aggregation Process

Abstract: The idea of designing novel anti-amyloid therapeutic agents has generated a lot of scientific interest and the potential for treating a variety of human pathophysiologies, such as neurodegenerative and non-neuropathic diseases linked to amyloid protein aggregation. To address this, different small molecules, peptides, surfactants, nanomaterials, etc., have been thoroughly investigated to learn more about their antiamyloidogenic capabilities, offering a great deal of potential for them to show up as future anti-amyloidogenic agents. In contrast to existing smallmolecule analogues, polymers have been envisaged as promising anti-amyloid agents for treating these diseases because of their enthralling physicochemical features and simplicity of functionalisation. This review article emphasises the latest developments in the design of synthetic polymers such as amino acid-conjugated polymers, glycopolymers, zwitterionic polymers and so on reported in the last decade in modulating amyloid aggregation process. Additionally, the structural function and mechanism involved in modifying the aggregation process are highlighted in order to inspire the researchers even more and provide insight into this important field of study.

Adaptive control method and experimental study of cone crusher based on aggregate online detection

The size of the discharge outlet of a cone crusher directly impacts the size of the aggregate produced. However, the discharge outlet is still adjusted manually, which has a significant error and affects production efficiency. For this reason, this study proposed an adaptive control method for cone crushers based on aggregate online detection. Firstly, the aggregate image was segmented using an instance segmentation model and the anchor and structure of the model were optimised. Then, this study proposed an evaluation method for quickly and accurately assessing the overall segmentation effect of network models. By comparing the results with those before optimisation, the accuracy of the optimised network model was improved from 0.923 to 0.940. Finally, an adaptive control experiment was conducted based on the online aggregate detection results. The experimental results showed that the discharge particle size distribution of the cone crusher becomes more stable after intelligent control is added, with the variance of the proportion of cumulative gradation at 15 mm decreased from 34.3 to 14.4. These results indicated that the developed adaptive control system effectively controls the fine processing of coarse aggregates and significantly improves the quality of aggregate crushing and processing.

Privacy‐preserving data aggregation achieving completeness of data queries in smart grid

AbstractIn smart grid systems, the control center formulates strategies and provides services by analyzing electricity consumption data. However, ensuring the privacy and security of user data is a critical concern. While traditional data aggregation schemes can provide a certain level of privacy protection for users, they also impose limitations on the control center's access to fine‐grained data. To address these challenges, we propose a privacy‐preserving data aggregation scheme supporting data query (PAQ). We designed a multi‐level data aggregation mechanism based on Paillier semi‐homomorphic encryption to achieve efficient aggregation of user data in the control center. Additionally, a data query mechanism based on electricity consumption intervals is introduced, allowing the control center to query aggregated ciphertexts for different user categories from outsourced data on the cloud server. Security analysis demonstrates that PAQ design effectively solves security issues in data aggregation and query processes. Performance analysis indicates that the proposed scheme outperforms existing solutions in terms of efficiency.

PFedBASC: Personalized Federated Learning with Blockchain-Assisted Semi-Centralized Framework

As network technology advances, there is an increasing need for a trusted new-generation information management system. Blockchain technology provides a decentralized, transparent, and tamper-proof foundation. Meanwhile, data islands have become a significant obstacle for machine learning applications. Although federated learning (FL) ensures data privacy protection, server-side security concerns persist. Traditional methods have employed a blockchain system in FL frameworks to maintain a tamper-proof global model database. In this context, we propose a novel personalized federated learning (pFL) with blockchain-assisted semi-centralized framework, pFedBASC. This approach, tailored for the Internet of Things (IoT) scenarios, constructs a semi-centralized IoT structure and utilizes trusted network connections to support FL. We concentrate on designing the aggregation process and FL algorithm, as well as the block structure. To address data heterogeneity and communication costs, we propose a pFL method called FedHype. In this method, each client is assigned a compact hypernetwork (HN) alongside a normal target network (TN) whose parameters are generated by the HN. Clients pull together other clients’ HNs for local aggregation to personalize their TNs, reducing communication costs. Furthermore, FedHype can be integrated with other existing algorithms, enhancing its functionality. Experimental results reveal that pFedBASC effectively tackles data heterogeneity issues while maintaining positive accuracy, communication efficiency, and robustness.

Influence of cultivation methods on the soil aggregate state in the context of weed development in winter wheat plantations

Weed control in winter wheat crops is an important issue. There is a risk of increasing populations of certain weed species that are resistant to some of herbicides used for winter wheat crops. This could be controlled by a combination of agronomic, mechanical, chemical, and biological methods. After introducing winter wheat into the rotation and improving tillage, the weediness of regular black soils with perennial root and emerged weeds were significantly reduced. The formation of nodal and rudimentary roots had a significant effect on the productivity of winter wheat under different soil moisture conditions. The highest yield of winter wheat was obtained when sown at the optimum time, with higher stem density and ear productivity due to better grain fullness. We studied what effects did the tillage methods have on the aggregate state of the soil in relation to weed development in winter wheat crops, finding that the structural and aggregate composition of the soil played an important role in winter wheat crops, influencing both the development of the crop root system and the water-physical balance of the chernozem, as well as naturally influencing the course of erosion processes in the experimental plots, and having a universal dynamic in terms of adaptation of aggregation and disaggregation processes. Prolonged mechanical stress on soil can cause destruction of its structure. For instance, continuous ploughing or moldboardless tillage with little or no manure application may permanently reduce soil fertility by increasing humus mineralisation. Subsequently, these factors may cause a significant decline in the soil's structural and aggregate composition, resulting in larger amounts of dusty particles smaller than 0.25 mm and cloddy particles larger than 10–12 mm. The soil's structural condition before sowing winter wheat in early September, on average for 2011–2016, indicates increased dispersion of the tilth layer (0–10 cm) in the experimental variants where shallow disc tillage of 10–12 cm was applied. Increase in the number of clods larger than 10 mm in the areas where moldboardless tillage had been applied can be attributed to significant soil drainage. The soil's aggregate state was rated as good, with 8.7% in the 0–10 cm soil layer and 1.7% of clods > 10 mm in the 0–30 cm layer. In 2014–2016, it was rated as satisfactory, with 7.4% and 9.8% of clods > 10 mm, respectively. Shallow disc cultivation resulted in slightly worse indicators: 6.6% and 8.3% of clods > 10 mm in soil layer 0–10 cm and 0–30 cm, respectively, in 2011–2013; and 7.2% and 6.9% of clods > 10 mm, respectively, in 2014–2016. In general, the parameters of optimal structural condition were positive. The tillage method used had a considerable effect on weed growth and development, particularly for those with a root and rhizome structure. It also affected the prevalence and development of pests and diseases in winter wheat. Agrotechnical methods of weed control do not guarantee complete destruction of weeds. Mechanical moldboardless tillage to the depth of 14–16 cm and disc tillage to the depth of 10–12 cm left the fields with 4.1 to 8.8 annual weeds per square meter and 1.3 to 3.3 specimens of harmful root weeds such as Convolvulus arvensis, Lactuca tatarica and Cirsium arvense. Mechanical moldboardless tillage to the depth of 14–16 cm and disc tillage to the depth of 10–12 cm left the fields with 4.1 to 8.8 annual weeds per square meter and 1.3 to 3.3 specimens of harmful root weeds such as Convolvulus arvensis, Lactuca tatarica and Cirsium arvense. Post-harvest residues (4–5 t/ha) can provide almost complete protection against weeds by covering the soil surface. However, pests and diseases may spread more easily due to preservation of fungal spores on the surface of plant residues and preservation of pest larvae in the straw and soil. The distribution of weed seeds in the soil was altered when the rotational tillage of common chernozems in winter wheat cultivation technology had been replaced with energy–saving minimum tillage (shallow flat-cutting disc tillage). This resulted in the concentration of most of the weed seeds (85–90%) in the upper soil layer (0–10 cm).

RANSAC-Based Planar Point Cloud Segmentation Enhanced by Normal Vector and Maximum Principal Curvature Clustering

Abstract. Planar feature segmentation is an essential task for 3D point cloud processing, finding many applications in various fields such as robotics and computer vision. The Random Sample Consensus (RANSAC) is one of the most common algorithms for the segmentation, but its performance, as given by the original form, is usually limited due to the use of a single threshold and interruption of similar planar features presented close to each other. To address these issues, we present a novel point cloud processing workflow which aims at developing an initial segmentation stage before the basic RANSAC is performed. Initially, normal vectors and maximum principal curvatures for each point of a given point cloud are analyzed and integrated. Subsequently, a subset of normal vectors and curvature is utilized to cluster planes with similar geometry based on the region growing algorithm, serving as a coarse but fast segmentation process. The segmentation is therefore refined with the RANSAC algorithm which can be now performed with higher accuracy and speed due to the reduced interference. After the RANSAC process is applied, resultant planar point clouds are built from the sparse ones via a point aggregation process based on geometric constraints. Four datasets (three real and one simulated) were used to verify the method. Compared to the classic segmentation method, our method achieves higher accuracy, with an RMSE from fitting equal to 0.0521 m, along with a higher recall of 93.31% and a higher F1-score of 95.38%.

Native ion mobility‐mass spectrometry reveals the binding mechanisms of anti‐amyloid therapeutic antibodies

AbstractOne of the most important attributes of anti‐amyloid antibodies is their selective binding to oligomeric and amyloid aggregates. However, current methods of examining the binding specificities of anti‐amyloid β (Aβ) antibodies have limited ability to differentiate between complexes that form between antibodies and monomeric or oligomeric Aβ species during the dynamic Aβ aggregation process. Here, we present a high‐resolution native ion‐mobility mass spectrometry (nIM‐MS) method to investigate complexes formed between a variety of Aβ oligomers and three Aβ‐specific IgGs, namely two antibodies with relatively high conformational specificity (aducanumab and A34) and one antibody with low conformational specificity (crenezumab). We found that crenezumab primarily binds Aβ monomers, while aducanumab preferentially binds Aβ monomers and dimers and A34 preferentially binds Aβ dimers, trimers, and tetrameters. Through collision induced unfolding (CIU) analysis, our data indicate that antibody stability is increased upon Aβ binding and, surprisingly, this stabilization involves the Fc region. Together, we conclude that nIM‐MS and CIU enable the identification of Aβ antibody binding stoichiometries and provide important details regarding antibody binding mechanisms.

Recent Developments of Hybrid Fluorescence Techniques: Advances in Amyloid Detection Methods.

Amyloid fibrils are formed from various pathological proteins. Monitoring their aggregation process is necessary for early detection and treatment. Among the available detection techniques, fluorescence is simple, intuitive, and convenient due to its sensitive and selective mode of detection. It has certain disadvantages like poor photothermal stability and detection state limitation. Research has focused on minimising the limitation by developing hybrid fluorescence techniques. This review focuses on the two ways fluorescence (intrinsic and extrinsic) has been used to monitor amyloid fibrils. In intrinsic/label free fluorescence: i) The fluorescence emission through aromatic amino acid residues like phenylalanine (F), tyrosine (Y) and tryptophan (W) is present in amyloidogenic peptides/protein sequence. And ii) The structural changes from alpha helix to cross-β-sheet structures during amyloid formation contribute to the fluorescence emission. The second method focuses on the use of extrinsic fluorophores to monitor amyloid fibrils i) organic dyes/small molecules, ii) fluorescent tagged proteins, iii) nanoparticles, iv) metal complexes and v) conjugated polymers. All these fluorophores havetheir own limitations. Developing them into hybrid fluorescence techniques and converting it into biosensors can contribute to early detection of disease.

Characterizing the oligomers distribution along the aggregation pathway of amyloid Aß1-40 by NMR.

This study delves into the early aggregation process of the Aβ1-40 amyloid peptide, elucidating the associated oligomers distribution. Motivated by the acknowledged role of small oligomers in the neurotoxic damage linked to Alzheimer's disease, we present an experimental protocol for preparing 26-O-acyl isoAβ1-40, a modified Aβ1-40 peptide facilitating rapid isomerization to the native amide form at neutral pH. This ensures seed-free solutions, minimizing experimental variability. Additionally, we demonstrate the efficacy of coupling NMR diffusion ordered spectroscopy (DOSY) with the Inverse Laplace Transform (ILT) reconstruction method, for effective characterization of early aggregation processes. This innovative approach efficiently maps oligomers distributions across a wide spectrum of initial peptide concentrations offering unique insights into the evolution of oligomers relative populations. As a proof of concept, we demonstrate the efficacy of our approach assessing the impact of Epigallocathechin gallate, a known remodeling agent of amyloid fibrils, on the oligomeric distributions of aggregated Aß1-40. The DOSY-ILT proposed approach stands as a robust and discriminating asset, providing a powerful strategy for rapidly gaining insight into potential inhibitors' impact on the aggregation process.

DISTINCTIVE FEATURES OF CELLULOSE NANOCRYSTALLITES

In this paper, a set of methods was used to study the structural characteristics and properties of cellulose nanocrystallites and free cellulose nanocrystalline particles (CNCs). It was shown that cellulose nanocrystallites have three main distinctive features. The first distinctive feature of cellulose nanocrystallites is their rod-like shape with a quite high aspect ratio and a low percolation threshold. The second distinctive feature of nanocrystallites is their highly developed specific surface area that leads to the spontaneous crystallization and aggregation of rod-like crystallites by their lateral planes. This aggregation process is thermodynamically favorable because it leads to a decrease in the specific surface area of nanocrystallites and a reduction of the thermodynamic potential. The third distinctive feature of cellulose nanocrystallites is the paracrystalline structure of their surface layers, which significantly affects structural characteristics such as lattice distortion, interplanar spacings, parameters, and volume of the crystalline unit cell, etc. Along with structure, the paracrystalline fraction affects also important physical and physical-chemical properties of cellulose, such as accessibility to deuteration, the content of CII-allomorph after cellulose alkalization, melting point of nanocrystallites, etc. Correlation equations were derived that provide to predict the structural characteristics and properties of nanocrystallites using the content of the paracrystalline fraction.

BFL-SA: Blockchain-based federated learning via enhanced secure aggregation

Federated learning, involving a central server and multiple clients, aims to keep data local but raises privacy concerns like data exposure and participation privacy. Secure aggregation, especially with pairwise masking, preserves privacy without accuracy loss. Yet, issues persist like security against malicious models, central server fault tolerance, and trust in decryption keys. Resolving these challenges is vital for advancing secure federated learning systems. In this paper, we present BFL-SA, a blockchain-based federated learning scheme via enhanced secure aggregation, which addresses key challenges by integrating blockchain consensus, publicly verifiable secret sharing, and an overdue gradients aggregation module. These enhancements significantly boost security and fault tolerance while improving the efficiency of data utilization in the secure aggregation process. After security analysis, we have demonstrated that BFL-SA achieves secure aggregation even in malicious models. Through experimental comparative analysis, BFL-SA exhibits rapid secure aggregation speed and achieves 100% model aggregation accuracy.

Method of aggregating temporal multimodal streaming data from iot devices based on reactive programming

Recent advancements in Internet of Things (IoT) technologies have led to an exponential increase in the generation of temporal multimodal streaming data from various IoT devices. Aggregating and processing this data efficiently poses significant challenges, including scalability, reliability, and synchronization issues. This paper proposes a novel method for aggregating temporal multimodal streaming data from IoT devices based on reactive programming principles. We begin by analyzing recent research and publications in the field to identify existing challenges and limitations in aggregating temporal data from IoT devices. The key problem addressed in this study is the efficient aggregation of data streams while ensuring scalability, reliability, and synchronization across different devices. The primary objective of this research is to develop a method that can hierarchically organize and aggregate temporal data streams from IoT devices while addressing the challenges of scalability and reliability. To achieve this, we leverage reactive programming techniques, specifically the GroupJoin operation, which allows for the seamless integration of data streams from multiple sources. Our method involves initializing the aggregation process by passing relevant data streams from two devices to the GroupJoin operation and configuring the settings for operation. Additionally, we introduce flexibility in the aggregation process by allowing for the hierarchical structuring of data streams based on physical or logical criteria. Furthermore, we discuss the implementation of our proposed method using the Rx library for .NET, which provides implementations for various programming languages, ensuring its adaptability across different platforms. We benchmark the performance of our method against a naive implementation, demonstrating its efficiency and scalability in aggregating temporal data streams from IoT devices. In conclusion, our proposed method offers a scalable and reliable solution for aggregating temporal multimodal streaming data from IoT devices. By leveraging reactive programming principles, we address key challenges in data aggregation and provide a flexible method for organizing and processing data streams efficiently.

Landscape metrics for land use/cover change and green space pattern in Can Tho city in 1998-2020 based on remote sensing and GIS

Changes in land cover/green space in Can Tho city in the period 1998-2020 were analyzed based on remote sensing data and the perspective of landscape ecology. The data collected from JAXA on land cover/land use classification entire Can Tho city in the period 1998-2020 was used to extract landscape metrics and to analyze the changes by a conversion matrix. The result shows a complex change in the urban green space in the period 1998 - 2020 in Can Tho. Research results show that Can Tho city had rice paddies and other croplands accounting for the majority of the land use structure. In the period 1998-2020, the city experienced a major shrinkage in croplands. Although other land use/cover types fluctuated differently, the fragmentation process still prevailed with fragments having discrete connections in space. The process of urbanization occurred with the expansion of built-up, but the concentration level was not high, with small fragments appearing sporadically distributed. However, comparing the landscape indices in the period 1998 – 2020 showed the aggregation process was increasingly taking place, and the level of fragmentation had tended to decrease. This trend was evidenced by the sharp increase in the number of patches, effective mesh size, and splitting index simultaneously with the gradual decline of the LPI index. The research results show high potential in expanding green space in Can Tho as well as application in planning urban green space development.

Photoreversible Aggregation of the Biliprotein Containing the First and Second GAF Domains of a Cyanobacteriochrome All2699 in Nostoc sp. PCC7120.

As plant photoreceptors, phytochromes are capable of detecting red light and far-red light, thereby governing plant growth. All2699 is a photoreceptor found in Nostoc sp. PCC7120 that specifically responds to red light and far-red light. All2699g1g2 is a truncated protein carrying the first and second GAF (cGMP phosphodiesterase/adenylyl cyclase/FhlA) domains of All2699. In this study, we found that, upon exposure to red light, the protein underwent aggregation, resulting in the formation of protein aggregates. Conversely, under far-red light irradiation, these protein aggregates dissociated. We delved into the factors that impact the aggregation of All2699g1g2, focusing on the protein structure. Our findings showed that the GAF2 domain contains a low-complexity (LC) loop region, which plays a crucial role in mediating protein aggregation. Specifically, phenylalanine at position 239 within the LC loop region was identified as a key site for the aggregation process. Furthermore, our research revealed that various factors, including irradiation time, temperature, concentration, NaCl concentration, and pH value, can impact the aggregation of All2699g1g2. The aggregation led to variations in Pfr concentration depending on temperature, NaCl concentration, and pH value. In contrast, ΔLC did not aggregate and therefore lacked responses to these factors. Consequently, the LC loop region of All2699g1g2 extended and enhanced sensory properties.

Exploration of the effect of NaCl/urea on aggregation process of imipramine hydrochloride drug and TX-165 mixture: A surface tension and UV–visible study

In this research, mixed micellization analysis of imipramine hydrochloride (IMP), an antidepressant drug with nonionic TX-165 surfactant has been investigated entirely via tensiometry and UV–visible techniques. Influence of distinct solvents of certain concentration (aqueous/50 mmol.kg−1 NaCl/500 mmol.kg−1 urea) was also detected on mixed association behavior of IMP+TX-165 mixtures. As compared to the aqueous system, higher nonideality in IMP+TX-165 in NaCl solution was detected while in the urea system was found less i.e., the interaction between IMP and TX-165 in different solvents was found as follows: NaCl > aqueous > urea. The fraction of TX-165 in mixed micelles (X1m) was found to be higher than IMP caused by the high hydrophobicity of TX-165 than IMP. The attained interaction parameter (βm) for bulk mixed systems was negative in each case depicting the synergism or attractive interaction amid IMP and TX-165. Numerous evaluated thermodynamic parameters demonstrated that IMP+TX-165 mixed micelles formation is viable along with spontaneous and among all employed solvents, spontaneity was found higher in NaCl solvent and lower in urea solvent than aqueous solution. Interaction of IMP with TX-165 was also detected by UV–visible analysis as an increase in absorbance was attained. Overall, results might be helpful in the development of the drug delivery system.

Molecular Design Using Selected Concentration Effects in Optically Activated Fluorescent Matrices.

Molecular physics plays a pivotal role in various fields, including medicine, pharmaceuticals, and broader industrial applications. This study aims to enhance the methods for producing specific optically active materials with distinct spectroscopic properties at the molecular level, which are crucial for these sectors, while prioritizing human safety in both production and application. Forensic science, a significant socio-economic field, often employs hazardous substances in analyzing friction ridges on porous surfaces, posing safety concerns. In response, we formulated novel, non-toxic procedures for examining paper evidence, particularly thermal papers. Our laboratory model utilizes a polyvinyl alcohol polymer as a rigid matrix to emulate the thermal paper's environment, enabling precise control over the spectroscopic characteristics of 1,8-diazafluoro-9-one (DFO). We identified and analyzed the cyclodimer 1,8-diazafluoren-9-one (DAK DFO), which is a non-toxic and biocompatible alternative for revealing forensic marks. The reagents used to preserve fingerprints were optimized for their effectiveness and stability. Using stationary absorption and emission spectroscopy, along with time-resolved emission studies, we verified the spectroscopic attributes of the new structures under deliberate aggregation conditions. Raman spectroscopy and quantum mechanical computations substantiated the cyclodimer's configuration. The investigation provides robust scientific endorsement for the novel compound and its structural diversity, influenced by the solvatochromic sensitivity of the DFO precursor. Our approach to monitoring aggregation processes signifies a substantial shift in synthetic research paradigms, leveraging simple chemistry to yield an innovative contribution to forensic science methodologies.

Raman spectroscopy in the study of amyloid formation and phase separation.

Neurodegenerative diseases, such as Alzheimer's and Parkinson's, share a common pathological feature of amyloid structure accumulation. However, the structure-function relationship between these well-ordered, β-sheet-rich, filamentous protein deposits and disease etiology remains to be defined. Recently, an emerging hypothesis has linked phase separation, a process involved in the formation of protein condensates, to amyloid formation, suggesting that liquid protein droplets serve as loci for amyloid initiation. To elucidate how these processes contribute to disease progression, tools that can directly report on protein secondary structural changes are needed. Here, we review recent studies that have demonstrated Raman spectroscopy as a powerful vibrational technique for interrogating amyloid structures; one that offers sensitivity from the global secondary structural level to specific residues. This probe-free technique is further enhanced via coupling to a microscope, which affords structural data with spatial resolution, known as Raman spectral imaging (RSI). In vitro and in cellulo applications of RSI are discussed, highlighting studies of protein droplet aging, cellular internalization of fibrils, and Raman imaging of intracellular water. Collectively, utilization of the myriad Raman spectroscopic methods will contribute to a deeper understanding of protein conformational dynamics in the complex cellular milieu and offer potential clinical diagnostic capabilities for protein misfolding and aggregation processes in disease states.

Molecular Dynamics Insights into the Aggregation Behavior of N-Terminal β-Lactoglobulin Peptides.

β-lactoglobulin (BLG) forms amyloid-like aggregates at high temperatures, low pH, and low ionic strengths. At a pH below 2, BLG undergoes hydrolysis into peptides, with N-terminal peptides 1-33 and 1-52 being prone to fibrillization, forming amyloid-like fibrils. Due to their good mechanical properties, BLG amyloids demonstrate great potential for diverse applications, including biosensors, nanocomposites, and catalysts. Consequently, further studies are essential to comprehensively understand the factors governing the formation of BLG amyloid-like morphologies. In this study, all-atom molecular dynamics simulations were employed to explore the aggregation of N-terminal 1-33 and 1-52 BLG peptides under conditions of pH 2 and at 10 mM NaCl concentration. The simulations revealed that the peptides spontaneously assembled into aggregates of varying sizes. The aggregation process was enabled by the low charge of peptides and the presence of hydrophobic residues within them. As the peptides associated into aggregates, there was a concurrent increase in β-sheet structures and the establishment of hydrogen bonds, enhancing the stability of the aggregates. Notably, on average, 1-33 peptides formed larger aggregates compared to their 1-52 counterparts, while the latter exhibited a slightly higher content of β-sheets and higher cluster orderliness. The applied approach facilitated insights into the early stages of amyloid-like aggregation and molecular-level insight into the formation of β-sheets, which serve as nucleation points for further fibril growth.

Two-Level Dynamic Programming-Enabled Non-Metric Data Aggregation Technique for the Internet of Things

The Internet of Things (IoT) has become a transformative technological infrastructure, serving as a benchmark for automating and standardizing various activities across different domains to reduce human effort, especially in hazardous environments. In these networks, devices with embedded sensors capture valuable information about activities and report it to the nearest server. Although IoT networks are exceptionally useful in solving real-life problems, managing duplicate data values, often captured by neighboring devices, remains a challenging issue. Despite various methodologies reported in the literature to minimize the occurrence of duplicate data, it continues to be an open research problem. This paper presents a sophisticated data aggregation approach designed to minimize the ratio of duplicate data values in the refined set with the least possible information loss in IoT networks. First, at the device level, a local data aggregation process filters out outliers and duplicates data before transmission. Second, at the server level, a dynamic programming-based non-metric method identifies the longest common subsequence (LCS) among data from neighboring devices, which is then shared with the edge module. Simulation results confirm the approach’s exceptional performance in optimizing the bandwidth, energy consumption, and response time while maintaining high accuracy and precision, thus significantly reducing overall network congestion.