Small Aggregates Research Articles

Acoustic analyses of mortars prepared with recycled aggregates

The use of aggregate types obtained from glass wastes and construction wastes was investigated in mortar mixtures. The sound absorption coefficient, sound transmission loss, flexural strength, compressive strength, open porosity ratio, density, luminance factor, and the coefficient of water absorption tests were performed in this study. The mortar content modifications, which are listed as binder ratio, aggregate gradation type, aggregate type, and fiber ratio, were examined. The influences of the mortar content changes on the measurement results were investigated. It was intended to improve the acoustic performance in line with mixture content modification. It was observed that as the binder ratio increases, the sound insulation performance and compressive strength can increase remarkably, and the sound insulation performance can improve especially at high frequencies. However, when the binder ratio increases, the sound absorption performance, open porosity ratio, and coefficient of water absorption can decrease in general terms. It was observed that using a very small aggregate particle size range, or using a well-graded aggregate mixture type instead of a uniformly-graded aggregate mixture type can reduce sound absorption performance. It was determined that the sound insulation level can increase as the aggregate particle size decreases, and the use of a well-graded aggregate mixture type rather than a uniform-graded aggregate mixture type is a good method to enhance compressive and flexural strength. It was mentioned that the utilization of lightweight aggregate in the mortar content can improve the sound absorption performance and reduce the sound transmission loss, compressive strength, and flexural strength performances. It was defined that the utilization of basalt or glass fiber in the mortar content can enhance sound absorption performance at high frequencies, sound insulation performance, and coefficient of water absorption.

Mutation of a Threonine Residue in αD-β4 Loop of Cyt2Aa2 Protein Influences Binding on Fluid Lipid Membranes.

Cyt proteins are insecticidal proteins originally from Bacillus thuringiensis. The lipid binding of the Cyt2Aa2 protein depends on the phase of the lipid bilayer. In this work, the importance of the conserved T144 residue in the αD-β4 loop for lipid binding on fluid lipid membranes was investigated via atomic force microscopy (AFM). Lipid membrane fluidity could be monitored for the following lipid mixture systems: POPC/DPPC, POPC/SM, and DOPC/SM. AFM results revealed that the T144A mutant was unable to bind to pure POPC bilayers. Similar topography between the wildtype and T144A mutant was seen for the POPC/Chol system. Small aggregates of T144A mutant were observed in the POPC and DOPC domains of the lipid mixture systems. In addition, the T144A mutant had no cytotoxic effect against human colon cancer cells. These results suggest that alanine replacement into threonine 144 hinders the binding of Cyt2Aa2 on liquid lipid membranes. These observations provide a possibility to modify the Cyt2Aa2 protein to specific cells via lipid phase selection.

Detection of Sargassum from Sentinel Satellite Sensors Using Deep Learning Approach

Since 2011, the proliferation of brown macro-algae of the genus Sargassum has considerably increased in the North Tropical Atlantic Sea, all the way from the Gulf of Guinea to the Caribbean Sea and the Gulf of Mexico. The large amount of Sargassum aggregations in that area cause major beaching events, which have a significant impact on the local economy and the environment and are starting to present a real threat to public health. In such a context, it is crucial to collect spatial and temporal data of Sargassum aggregations to understand their dynamics and predict stranding. Lately, indexes based on satellite imagery such as the Maximum Chlorophyll Index (MCI) or the Alternative Floating Algae Index (AFAI), have been developed and used to detect these Sargassum aggregations. However, their accuracy is questionable as they tend to detect various non-Sargassum features. To overcome false positive detection biases encountered by the index-thresholding methods, we developed two new deep learning models specific for Sargassum detection based on an encoder–decoder convolutional neural network (CNN). One was tuned to spectral bands from the multispectral instrument (MSI) onboard Sentinel-2 satellites and the other to the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3 satellites. This specific new approach outperformed previous generalist deep learning models, such as ErisNet, UNet, and SegNet, in the detection of Sargassum from satellite images with the same training, with an F1-score of 0.88 using MSI images, and 0.76 using OLCI images. Indeed, the proposed CNN considered neighbor pixels, unlike ErisNet, and had fewer reduction levels than UNet and SegNet, allowing filiform objects such as Sargassum aggregations to be detected. Using both spectral and spatial features, it also yielded a better detection performance compared to algal index-based techniques. The CNN method proposed here recognizes new small aggregations that were previously undetected, provides more complete structures, and has a lower false-positive detection rate.

Monoglyceride Lipase Deficiency Is Associated with Altered Thrombogenesis in Mice.

Monoglyceride lipase (MGL) hydrolyzes monoacylglycerols (MG) to glycerol and one fatty acid. Among the various MG species, MGL also degrades 2-arachidonoylglycerol, the most abundant endocannabinoid and potent activator of the cannabinoid receptors 1 and 2. We investigated the consequences of MGL deficiency on platelet function using systemic (Mgl-/-) and platelet-specific Mgl-deficient (platMgl-/-) mice. Despite comparable platelet morphology, loss of MGL was associated with decreased platelet aggregation and reduced response to collagen activation. This was reflected by reduced thrombus formation in vitro, accompanied by a longer bleeding time and a higher blood volume loss. Occlusion time after FeCl3-induced injury was markedly reduced in Mgl-/- mice, which is consistent with contraction of large aggregates and fewer small aggregates in vitro. The absence of any functional changes in platelets from platMgl-/- mice is in accordance with lipid degradation products or other molecules in the circulation, rather than platelet-specific effects, being responsible for the observed alterations in Mgl-/- mice. We conclude that genetic deletion of MGL is associated with altered thrombogenesis.

Changes in soil surface properties under simulated rainfall and the effect of surface roughness on runoff, infiltration and soil loss

Soil erosion by water is a result of detachment of particles or small aggregates from the soil surface followed by transport of the detached material. One of the elements that affects surface runoff and soil erosion is the soil surface roughness (SSR). Prior research reports that increasing SSR reduces generation of runoff and soil loss. In addition to that, it is widely reported that across-slope oriented roughness is better at controlling soil and water losses. However, to date there have been few studies into the effect of both magnitude and orientation of SSR on runoff, infiltration and soil erosion at the sub process level (i.e. by raindrop splash and overland flow), occurring simultaneously. In this study, the effects of up-down-slope oriented SSR (Treatment A), across-slope oriented SSR (Treatment B) and random SSR (Treatment C) were compared, along with a smooth surface (Treatment D). A moderate slope gradient of 10 %, a simulated rainfall intensity of 90 mm hr−1 and storm durations of 15 or 30 min were considered. The SSR was measured using the chain method, before and after the rainfall event. Images of the soil surface were taken using a hand-held laser scanner to monitor the effect of rainfall on the surface morphology. The outcome of this study shows that rainfall erosivity increases the SSR of the initially smooth surface, but decreases that of the initially rough surface, particularly in the random SSR treatment, where the decrease in SSR was 64 % of the pre-rainfall condition. This was due to the effects of raindrop impacts and overland flow. The random SSR treatment generated significantly more runoff and soil loss, and less infiltration than all other treatments (p < 0.001), but for raindrop splash erosion, there was no significant difference between random SSR and the other treatments. Contrary to expectations, the across-slope oriented SSR did not always reduce runoff and soil erosion compared to the up-down-slope orientation. This can be explained by degradation of surface microtopography by rainfall and runoff, as confirmed by the post-rainfall SSR measurements.

Aggregation of Asphaltene Subfractions A1 and A2 in Different Solvents from the Perspective of Molecular Dynamics Simulations

The aggregation behavior of model molecules of asphaltene subfractions A1 and A2 dissolved in heptane, toluene, and tetrahydrofuran (THF) were studied using molecular dynamics simulations. The proposed asphaltene molecular models are based on previously studied structures with two new models, including a highly aromatic model with a prominent island-type molecule and another molecule with a prominent archipelago-type architecture. The aggregation mechanisms in toluene, THF, and heptane solvents were studied. The results in heptane and toluene were consistent with the solubility of asphaltenes and their subfractions in these solvents. The size of the aggregates is well-correlated with aromaticity. When considering THF, large aggregates are broken down into smaller aggregates. This could lead to the mixture of high, medium, and low molecular weight distribution bands usually observed when gel permeation chromatography (GPC) analyses are conducted on asphaltene samples in THF. The size distribution extracted from the simulations shows a bimodal distribution with profiles similar to the size distribution profiles usually found in GPC analysis for asphaltene samples in THF. The distributions of dipole moments of the aggregates against the number of molecules in aggregates were constructed in both THF and toluene and reveal that the dipole moment of the aggregates vanishes when the number of molecules increases as a result of a random structure of the aggregates. The contributions of different molecular interactions to the aggregation mechanism, such as π stacking, van der Waals, and hydrogen bonds, are described.

Ultrastructural study of anther parasitism of Ficus laevigata by Ficophagus laevigatus (Aphelenchoididae).

Transmission electron microscopy (TEM) was used to compare the ultrastructural differences between healthy male florets (anthers) and one floret parasitized by Ficophagus laevigatus in late phase C syconia of Ficus laevigata from southern Florida. Previous light-microscopic examination of paraffin-sectioned material showed that F. laevigatus-infested anthers of F. laevigata manifested as malformed, often with aberrant pollen and hypertrophied epidermal cells closest to regions containing propagating nematodes. Female florets or fig wasp-parasitized female florets were not observed to be parasitized by nematodes. Considering that plant-feeding in the Aphelenchoididae is purportedly much less specialized than in certain groups of the Tylenchomorpha, where specialized hypertrophied feeder cells are produced in response to nematode feeding, we examined the putative induced response in this unusual aphelenchoidid system with the higher resolution afforded by TEM. TEM confirmed the expression of significant epidermal cell hypertrophy of the anther and anther filament in the presence of propagating nematodes, which was expressed as cell enlargement (2-5X), fractionation of large electron-dense stores into smaller aggregates, irregularly shaped nuclei enclosed by an elongated nuclear envelope, nucleolus enlargement, increased organelle production, and apparent metabolism with increased numbers of mitochondria, pro-plastids, and endoplasmic reticulum, as well as increased thickening of the cell walls. Pathological effects were observed in adjacent cells/tissue (e.g., anther and anther filament parenchymal cells, pollen tubes, pollen, and endothecium) with apparent diminishment as the distance from propagating nematodes increased (which was also probably affected by number of nematodes). Some TEM sections captured previously undocumented ultrastructural highlights of propagating individuals of F. laevigatus.

Cellular models for the investigation of the structural dynamics and activity of human tau protein aggregate formation.

Evidence for the significant role of tau protein in Alzheimer's disease (AD) highlights the need for deep investigation of intracellular tau aggregation pathways and formation of aberrant tau polymorphs within cellular models. Intrinsically disordered proteins with low sequence complexity like hyperphosphorylated tau protein can form condensed folded structures that contribute to neurodegeneration. Candidates for aberrant intracellular tau folding and aggregation have been identified including liquid-liquid phase separation (LLPS) and we will investigate if nanocrystal growth at ribosomes could be observed. Recent research discovered that the differences in tau folding at the core of pathological tau aggregates can differentiate AD from other tauopathies. It is hypothesized that disease specific changes in the cellular environment surrounding tau, such as patterns of hyperphosphorylation, could lead to differential core structures. Combining advanced structural determination methods including cryoEM, cryoFIB, and cryo-electron tomography, we are working on direct imaging of intracellular tau aggregates in cells as well as autopsy tissue derived from human brain. The goal is to develop new cellular models of human tau aggregation using both mammalian and insect cell lines which we employ to evaluate intracellular forces necessary to create unique pathological tau structures beginning with patterns of hyperphosphorylation. We overexpressed the six human tau isoforms in both Sf9 insect cells and SH-SY5Y mammalian cells and induced this intracellular tau to form aggregates by incubating the cells with a small molecule tau aggregation inducer Congo Red. Using cryo-focused ion beam, we can mill thin lamellae through vitrified cells used for future cryo-tomography imaging and 3D reconstruction. Using advanced structural methods, we can compare our aggregates with authentic human brain-derived AD tau aggregates as well as visualize intracellular interactions.

Oligomeric state and chaperon activity of Hsp60.

Heat-shock proteins are capable to exert their stabilizing action against biomolecules aggregation through different mechanisms, which are generally related to their molecular weight. Similar to its bacterial homologous GroEl, Hsp60 is known to work as a folding machine with the assistance of co-chaperonin Hsp10 and ATP. However, recent experimental evidence suggested an alternative mechanism exploited by Hsp60 towards aggregation prone molecules. Indeed, we already observed that Hsp60 stabilized Aβ40 against fibrillization in the absence of ATP by recruiting early small aggregates of Ab, Here we investigate the mechanism exerted by Hsp60 against the amyloid fibrillization in relation with its oligomeric structure. To this purpose, the monomeric or tetradecameric form of the protein were isolated and their effect on beta-amyloid aggregation was tested separately. The structural stability of the two forms of Hsp60 was also investigated by differential scanning calorimetry (DSC), Light Scattering and Circular Dichroism. Results shown that the monomeric form is less stable but more effective against amyloid fibrillization. A major functionality related to a less ordered and stable structure is a common treat of proteins having intrinsically disordered regions (IDR). In Hsp60 contacts between subunits in the equatorial domain have been recognized as the region of moonlighting character. Their exposure to the solvent confers to monomeric Hsp60 the ability to interact with amyloid molecules at a major extent than that due to the plain increase of the total protein surface.

Biosurfactants as Multifunctional Remediation Agents of Environmental Pollutants Generated by the Petroleum Industry

Fuel and oil spills during the exploration, refining, and distribution of oil and petrochemicals are primarily responsible for the accumulation of organic pollutants in the environment. The reduction in contamination caused by hydrocarbons, heavy metals, oily effluents, and particulate matter generated by industrial activities and the efficient recovery of oil at great depths in an environmentally friendly way pose a challenge, as recovery and cleaning processes require the direct application of surface-active agents, detergents, degreasers, or solvents, often generating other environmental problems due to the toxicity and accumulation of these substances. Thus, the application of natural surface-active agents is an attractive solution. Due to their amphipathic structures, microbial surfactants solubilize oil through the formation of small aggregates (micelles) that disperse in water, with numerous applications in the petroleum industry. Biosurfactants have proven their usefulness in solubilizing oil trapped in rock, which is a prerequisite for enhanced oil recovery (EOR). Biosurfactants are also important biotechnological agents in anti-corrosion processes, preventing incrustations and the formation of biofilms on metallic surfaces, and are used in formulations of emulsifiers/demulsifiers, facilitate the transport of heavy oil through pipelines, and have other innovative applications in the oil industry. The use of natural surfactants can reduce the generation of pollutants from the use of synthetic detergents or chemical solvents without sacrificing economic gains for the oil industry. Therefore, investments in biotechnological processes are essential. It is predicted that, in the not-too-distant future, natural surfactants will become viable from an economic standpoint and dominate the world market. The application of biosurfactants in these settings would lead to industrial growth and environmental sustainability. The main goal of this paper is to provide an overview of diverse applications of biosurfactants on environmental remediation, petroleum biotechnology, and the oil industry through a scientific literature review.

A proteome scale study reveals how plastic surfaces and agitation promote protein aggregation

Protein aggregation in biotherapeutics can reduce their activity and effectiveness. It may also promote immune reactions responsible for severe adverse effects. The impact of plastic materials on protein destabilization is not totally understood. Here, we propose to deconvolve the effects of material surface, air/liquid interface, and agitation to decipher their respective role in protein destabilization and aggregation. We analyzed the effect of polypropylene, TEFLON, glass and LOBIND surfaces on the stability of purified proteins (bovine serum albumin, hemoglobin and α-synuclein) and on a cell extract composed of 6000 soluble proteins during agitation (P = 0.1–1.2 W/kg). Proteomic analysis revealed that chaperonins, intrinsically disordered proteins and ribosomes were more sensitive to the combined effects of material surfaces and agitation while small metabolic oligomers could be protected in the same conditions. Protein loss observations coupled to Raman microscopy, dynamic light scattering and proteomic allowed us to propose a mechanistic model of protein destabilization by plastics. Our results suggest that protein loss is not primarily due to the nucleation of small aggregates in solution, but to the destabilization of proteins exposed to material surfaces and their subsequent aggregation at the sheared air/liquid interface, an effect that cannot be prevented by using LOBIND tubes. A guidance can be established on how to minimize these adverse effects. Remove one of the components of this combined stress - material, air (even partially), or agitation - and proteins will be preserved.

Use of Sewage Sludge for the Substitution of Fine Aggregates for Concrete

This work analyzes the use of sewage sludge, generated in wastewater treatment plants, as an alternative for small aggregate to be used in concrete. Concrete cylindrical specimens with height h = 20 cm and diameter D = 10 cm were prepared using different amounts of sludge in the substitution of fine aggregates. Portland cement (CP II Z 32 RS cement) was used in all concrete mixtures, and two water-cement ratios and four cement-sludge mixtures were investigated. Compressive strength, sclerometer index, ultrasonic wave transmission velocity, and water absorption capillary tests were performed. The results showed that the use of sewage sludge as a replacement for fine aggregate to produce concrete exhibited a positive effect on both its compressive strength and its capillary water absorption. The results, even preliminary ones, demonstrated that the sludge could be used as an effective replacement for fine aggregate to produce concrete. The replacement of fine aggregate with 5% sewage sludge proved to be the optimal replacement value for the type of sewage sludge investigated.

How does aggregation of doxorubicin molecules affect its solvation and membrane penetration?

Aggregation and dynamics of Doxorubicin in solvents like water, DMF, EtOH, DMSO and at DPPC/water and octanol/water interfaces were studied using molecular dynamics (MD) simulations.

Population distribution, aggregation sites and seasonal occurrence of Australia’s western population of the grey nurse shark Carcharias taurus

Western Australia (WA) is host to a genetically discrete population of grey nurse sharks Carcharias taurus, listed by the IUCN as Near Threatened based on mean annual catch rates prior to protection within Australian waters in 1997. Only one small aggregation has been documented in WA to date with little data available on population dynamics, or spatial and temporal distribution along the WA coastline. We analysed C. taurus data from 16 years (2006-2021) of diver observations (2347 sightings) and commercial fisheries bycatch records (574 sharks), finding evidence of 4 new aggregation sites. Sightings at Shark Cave, a popular dive site near Perth, were related to water temperature and time of year (non-linear), with sightings per unit effort generally higher during the warmer months of summer and autumn (20-22°C). Evidence of fishing injuries was found on 17% of all C. taurus photo-tagged at Shark Cave. Strong site philopatry was apparent at most sites, and included a male shark that returned for 12 consecutive years to the Exmouth Navy Pier. This study confirms the range of Australia’s western population of C. taurus from near the South Australia/WA border, north to Shark Bay, with distribution extending further north along the coast when sea temperatures are lower (May-December). The beneficial role of citizen science in large observation studies such as this one is supported. However, while this research greatly increases the C. taurus knowledge base in WA, a large information deficit still exists. The identification and assessment of further aggregation sites is recommended, underpinning future management and protection measures for this iconic species.

Classification of whole slide images of breast histopathology based on spatial correlation characteristics

目的 病理学检查是明确乳腺癌诊断及肿瘤类型的金标准。深度神经网络广泛应用于乳腺病理全切片的诊断工作并取得了明显进展，但是现有大多数工作只是将全切片切割成小图像块，对每个图像块进行单独处理，没有考虑它们之间的空间信息。为此，提出了一种融合空间相关性特征的乳腺组织病理全切片分类方法。方法 首先基于卷积神经网络对病理图像块进行预测，并提取每个图像块有代表性的深层特征，然后利用特征融合将图像块及其周围图像的特征进行聚合，以形成具有空间相关性的块描述符，最后将全切片图像中最可疑的块描述符传递给循环神经网络，以预测最终的全切片级别的分类。结果 本文构建了一个经过详细标注的乳腺病理全切片数据集，并在此数据集上进行良性/恶性二分类实验。在自建的数据集中与3种全切片分类方法进行了比较。结果表明，本文方法的分类精度达到96.3%，比未考虑空间相关性的方法高出了1.9%，与基于热力图特征和基于空间性和随机森林的方法相比，分类精度分别高出8.8%和1.3%。结论 本文提出的乳腺组织病理全切片识别方法将空间相关性特征融合和RNN分类集成到一个统一模型，有助于提高图像识别准确率，为病理图像诊断工作提供了高效的辅助诊断工具。;Objective Pathological examination can be as the“gold standard”to interpret breast cancer diagnosis and its tumor types. To improve the treatment effect of breast cancer，accurate interpretation is beneficial to clarify the type of the disease early and effectively. However，it is time-consuming to check the pathological whole slide images（WSIs）manually，and the diagnosis result is easily affected by personal experience. Due to computer vision-based convolutional neural networks（CNNs）are applied to the classification task of histopathological images computer-aided diagnostic（CAD）techniques for digital images of histopathology has been developing intensively. However，it is still challenged to split largesized WSIs into small patches and each patch is processed individually without the spatial information between them. Also， the information-involved of the patches learned is not utilized in the small size patch feature aggregation process. To resolve these problems，we develop a spatial correlation-based classification method for breast histopathology images，which can re-identify the WSIs classification problem through deep learning feature fusion and recurrent neural network（RNN）based classification. Method Our demonstration consists of four aspects：1）pre-processing operation of WSIs，2）CNN-based breast patch prediction，3）image patch feature fusion，and 4）RNN-based WSIs classification. First，it is focused on a preprocessing operation on whole slide images of breast pathology，using a sliding window to cut the WSIs into patches of a suitable size in terms of a CNN. Second，CNN-based patch prediction is used to predict the possibility of cancer in each patch and each patch is encoded as a fixed-length feature. To reduce unpredictable class of cancerous patch being classified as non-cancerous patch，the ResNet34 is used as the patch classification model and penalty factors is added in Focal loss. Third，the feature fusion of image blocks can be recognized as a feature fusion niche that takes the entire grid of patches as input and the spatial correlation of patches and their surrounding patches are as feature fusion of all patches within the grid，thus block descriptors are formed. Feature fusion methods are involved in，including 1）Weight，2）Max， 3）Avg，4）Norm3，and 5）WeightNorm3. Finally，the RNN-based WSIs classification is used to pass the block feature descriptors with high probability of cancer in each WSIs to the RNN，and the RNN model is used to learn the sequence relationship between the image block feature sequences. It can expand the processing field of view of the classification model to multiple Blocks and optimize the classification accuracy of breast pathology WSIs. Additionally，we design and develop an online recognition system for assistance. Result A detailed annotated whole slide images dataset of breast pathology is constructed，and benign/malignant dichotomous classification experiments are carried out on this dataset. The results are compared to three WSIs classification methods in the self-constructed dataset，and it shows that the classification accuracy of the method can be reached to 96. 3%，which is 1. 9% higher than non-spatial correlation method. The classification accuracy of each is 8. 8% and 1. 3% higher compared to heat map features-based methods and spatial correlation and random forest-related methods. Conclusion The proposed breast pathology recognition method can improve image recognition accuracy and provide an efficient diagnostic aid for pathology image diagnosis work，which integrates feature fusion and RNN classification into a synthesized model.

The inhibitory effects of platinum(II) complexes on amyloid aggregation: a theoretical and experimental approach.

Platinum (Pt)(II) square planar complexes are well-known anticancer drugs whose Mechanism of Action (MOA) are finely tuned by the polar, hydrophobic and aromatic features of the ligands. In the attempt to translate this tunability to the identification of potential neurodrugs, herein, four Pt(II) complexes were investigated in their ability to modulate the self-aggregation processes of two amyloidogenic models: Sup35p7-13 and NPM1264-277 peptides. In particular, phenanthriplatin revealed the most efficient agent in the modulation of amyloid aggregation: through several biophysical assays, as Thioflavin T (ThT), electrospray ionization mass spectrometry (ESI-MS) and ultraviolet-visible (UV-vis) absorption spectroscopy, this complex revealed able to markedly suppress aggregation and to disassemble small soluble aggregates. This effect was due to a direct coordination of phenanthriplatin to the amyloid, with the loss of several ligands and different stoichiometries, by the formation of π-π and π-cation interactions as indicated from molecular dynamic simulations. Presented data support a growing and recent approach concerning the repurposing of metallodrugs as potential novel neurotherapeutics.

Dioctyl sodium sulfosuccinate surfactant self-assembly dependency of solvent hydrophilicity: a modelling study.

The self-assembly of dioctyl sodium sulfosuccinate (AOT) model surfactant in solvent environments of differing polarity is examined by means of dissipative particle dynamics (DPD) bead model parametrized against Hildebrand solubility parameters from atomistic molecular dynamics (MD) simulations. The model predicts that in hydrophobic solvents (e.g. dodecane) the surfactant forms small (Nagg ∼ 8) reverse micellar aggregates, while in a solvent corresponding to water lamellar assembly takes place, in good agreement with literature structural parameters. Interestingly, solvents of intermediate polarity lead to formation of large, internally structured aggregates. In these, the surfactant headgroups cluster within the aggregate, surrounded by a continuous phase formed by the hydrocarbon tails. We show that the partitioning of the headgroups between the aggregate surface layer and the inner clustered phase depends primarily on solvent polarity, and can be controlled by the solvent, but also system composition. Finally, we compare the DPD assembly response to simplified effective interaction potentials derived at dilute concentration limit for the interactions. The comparison reveals that the simplified effective potential descriptions provide good level of insight on the assembly morphologies, despite drastic, isotropic interactions simplification involved.

Robustness of heteroaggregates involving hydrophobic cholesterol and its mimetics.

Exploring the self and cross aggregation affinity of cholesterol (CHL) and some of its lookalikes, e.g., cholesteryl hemisuccinate (CHM), campesterol (CAM) and arjunic acid (ARJ), provides crucial understanding towards the influence of weak forces in inducing mixed micellization through heteroaggregation. Strongly hydrophobic CHL, with a benchmark inclination towards aggregation, often forms detrimental plaques in crucial human organs that are fairly difficult to disintegrate. Traditionally known anti-dyslipidemic agents like CAM and ARJ are known to interact strongly with CHL in the gut when ingested. They further form mixed micelles along with the bile components and interfere with the CHL absorption across the epithelial cell layer of the intestine. Some invariant questions like how robust are the heteroaggregates formed between these mimetics and CHL are very important to appreciate the efficacy of such anti-dyslipidemic agents. In this work using molecular dynamics simulations and varied structural analysis, we characterize the heteroaggregates. Simulations indicate that CHL-CHM mixed assemblies are comparatively bigger and significantly stabilized by strong electrostatic and favourable vdW forces. Small and diffused CHL-ARJ aggregates are observed in our simulations with a not so favourable energetics, indicating a possible attenuation pathway of CHL aggregation in the presence of ARJ.

One- and two-photon lasing from a TCF-based AIE dye

The aggregation of a TCF-based dye exhibiting AIE is used to enhance the random lasing emission intensity up to 170 times. The transition from small aggregates to crystals forms a second lasing band and is responsible for 2-photon pumped lasing.

Distributed Learning Over Networks With Graph-Attention-Based Personalization

In conventional distributed learning over a network, multiple agents collaboratively build a common machine learning model. However, due to the underlying non-i.i.d. data distribution among agents, the unified learning model becomes inefficient for each agent to process its locally accessible data. To address this problem, we propose a graph-attention-based personalized training algorithm (GATTA) for distributed deep learning. The GATTA enables each agent to train its local personalized model while exploiting its correlation with neighboring nodes and utilizing their useful information for aggregation. In particular, the personalized model in each agent is composed of a global part and a node-specific part. By treating each agent as one node in a graph and the node-specific parameters as its features, the benefits of the graph attention mechanism can be inherited. Namely, instead of aggregation based on averaging, it learns the specific weights for different neighboring nodes without requiring prior knowledge about the graph structure or the neighboring nodes' data distribution. Furthermore, relying on the weight-learning procedure, we develop a communication-efficient GATTA by skipping the transmission of information with small aggregation weights. Additionally, we theoretically analyze the convergence properties of GATTA for non-convex loss functions. Numerical results validate the excellent performances of the proposed algorithms in terms of convergence and communication cost.