Single Aggregation Research Articles

Aggregation-Induced Emissive Feringa-Type Motor: Toward the Dual-Functional Motor in a Single Molecular Aggregation System.

Aggregation-induced emission (AIE)allows tunable photoluminescence via the simple regulation of molecular aggregation. The research spurt along this vein has also offered tremendous opportunities for light-responsive artificial molecular machines that are to be fully explored for performing versatile functions. Herein, the study reports a light-driven Feringa-type motor, when in the appropriate aggregation state, not only demonstrates the light-activated rotary motion but emits photons with good quantum yield. A semi-quantitative TD-DFT calculation is also conducted to aid the understanding of the competitive photoluminescence and photoisomerization processes of the motor. Cytotoxicity test shows this motor possesses good biocompatibility, laying a solid foundation for applying it in the bio-environment. The results demonstrated that the engagement of the aggregation-induced emission concept and light-driven Feringa-motor can lead to the discovery of the novel motorized AIEgen, which will further stimulate the rise of more advanced molecular motors capable of executing multi-functionalities.

MD-GCCF: Multi-view deep graph contrastive learning for collaborative filtering

Collaborative Filtering (CF), a classical recommender system approach, learns users’ interests and behavioral preferences for items through a user–item interaction graph. CF based on graph neural network (GNN) and CF based on graph contrastive learning (GCL) show strong advantages in both modeling multi-layer signals and solving label sparsity, respectively. However, there are still two key problems to be solved: Most CF models based on (1) GNN suffer from the over-smoothing problem and are unable to aggregate deep collaborative signals and (2) GCL adopts a single aggregation paradigm, resulting in a lack of diversity in the feature representation of collaborative signals. To solve the above problems, a multi-view deep graph contrastive learning for collaborative filtering (MD-GCCF) has been proposed from two perspectives. First, a deep graph collaborative signal aggregation module is proposed to learn potential intention similarity representations for deep collaborative signal propagation within a few layers. Second, a novel multi-view contrastive learning module has been proposed, utilizing both local and global contrastive learning views from the collaborative signal aggregation module to enhance deep structures and semantic features in collaborative signals. MD-GCCF improves by 9.52%, 3.34%, and 2.49% compared to the rival models, respectively, in the Amazon book, Yelp2018, and Gowalla datasets.

Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers

Byzantine machine learning has garnered considerable attention in light of the unpredictable faults that can occur in large-scale distributed learning systems. The key to secure resilience against Byzantine machines in distributed learning is resilient aggregation mechanisms. Although abundant resilient aggregation rules have been proposed, they are designed in ad-hoc manners, imposing extra barriers on comparing, analyzing, and improving the rules across performance criteria. This paper studies near-optimal aggregation rules using clustering in the presence of outliers. Our outlier-robust clustering approach utilizes geometric properties of the update vectors provided by workers. Our analysis show that constant approximations to the 1-center and 1-mean clustering problems with outliers provide near-optimal resilient aggregators for metric-based criteria, which have been proven to be crucial in the homogeneous and heterogeneous cases respectively. In addition, we discuss two contradicting types of attacks under which no single aggregation rule is guaranteed to improve upon the naive average. Based on the discussion, we propose a two-phase resilient aggregation framework. We run experiments for image classification using a non-convex loss function. The proposed algorithms outperform previously known aggregation rules by a large margin with both homogeneous and heterogeneous data distributions among non-faulty workers. Code and appendix are available at https://github.com/jerry907/AAAI24-RASHB.

Enhancing Consumer Privacy in Federated Load Forecasting Through Single Layer Aggregation

Enhancing Consumer Privacy in Federated Load Forecasting Through Single Layer Aggregation

Dual Aggregations of a Near-Infrared Aggregation-Induced Emission Luminogen for Enhanced Imaging of Alzheimer's Disease.

Aggregation-induced emission (AIE) enables "Turn-On" imaging generally through single aggregation of the AIE luminogen (AIEgen). Dual aggregrations of the AIEgen might further enhance the imaging intensity and the consequent sensitivity. Herein, we rationally designed a near-infrared (NIR) AIEgen Ac-Trp-Glu-His-Asp-Cys(StBu)-Pra(QMT)-CBT (QMT-CBT) which, upon caspase1 (Cas1) activation, underwent a CBT-Cys click reaction to form cyclic dimers QMT-Dimer (the first aggregation) and assembled into nanoparticles (the second aggregation), turning the AIE signal "on" for enhanced imaging of Alzheimer's disease (AD). Molecular dynamics simulations validated that the fluorogen QMT in QMT-NPs stacked much tighter with each other than in the single aggregates of the control compound Ac-Trp-Glu-His-Asp-Cys(tBu)-Pra(QMT)-CBT (QMT-CBT-Ctrl). Dual aggregations of QMT rendered 1.9-, 1.7-, and 1.4-fold enhanced fluorescence intensities of its single aggregation in vitro, in cells, and in a living AD mouse model, respectively. We anticipate this smart fluorogen to be used for sensitive diagnosis of AD in the clinic in the near future.

Penetrance and expressivity of mitochondrial variants in a large clinically unselected population.

Whole genome sequencing (WGS) from large clinically unselected cohorts provides a unique opportunity to assess the penetrance and expressivity of rare and/or known pathogenic mitochondrial variants in population. Using WGS from 179 862 clinically unselected individuals from the UK Biobank, we performed extensive single and rare variant aggregation association analyses of 15 881 mtDNA variants and 73 known pathogenic variants with 15 mitochondrial disease-relevant phenotypes. We identified 12 homoplasmic and one heteroplasmic variant (m.3243A>G) with genome-wide significant associations in our clinically unselected cohort. Heteroplasmic m.3243A>G (MAF = 0.0002, a known pathogenic variant) was associated with diabetes, deafness and heart failure and 12 homoplasmic variants increased aspartate aminotransferase levels including three low-frequency variants (MAF ~0.002 and beta~0.3 SD). Most pathogenic mitochondrial disease variants (n = 66/74) were rare in the population (<1:9000). Aggregated or single variant analysis of pathogenic variants showed low penetrance in unselected settings for the relevant phenotypes, except m.3243A>G. Multi-system disease risk and penetrance of diabetes, deafness and heart failure greatly increased with m.3243A>G level ≥ 10%. The odds ratio of these traits increased from 5.61, 12.3 and 10.1 to 25.1, 55.0 and 39.5, respectively. Diabetes risk with m.3243A>G was further influenced by type 2 diabetes genetic risk. Our study of mitochondrial variation in a large-unselected population identified novel associations and demonstrated that pathogenic mitochondrial variants have lower penetrance in clinically unselected settings. m.3243A>G was an exception at higher heteroplasmy showing a significant impact on health making it a good candidate for incidental reporting.

What’s in the bee nest holes? A single aggregation of Megachile parietina reveals and helps to fill up Eltonian shortfalls

Megachile (Chalicodoma) parietina (Geoffroy, 1785) is a Palearctic solitary bee included in the Red List of some central European Countries. Females build durable nests, reused year after year, by mixing soil with a salivary secretion. Like for most solitary bees, the resources contained within M. parietina nests attract several other insects which exploit pollen supplies or feed on the immature brood. These associated insects have mainly been studied for mantained bees and considered for their effect on the host reproductive success.A very large nesting aggregation of M. parietina in Central Tuscany has been studied for three consecutive years. We have identified 32 associated insect species, which certainly are an underestimate of the species present. Among the identified species, only eight had been previously reported for M. parietina. All the species were classified both according to the specificity for the host taxon (Chalicodoma, Megachilidae, Anthophila, Hymenoptera, Others) and to the ecological relationship (cleptoparasites, parasitoids, predators of larvae, food commensal, scavengers, and occasional nest users).This highlighted both the richness of the ecological network within the nesting aggregation and the value of studying these nesting sites to fill Eltonian shortfalls, i.e. the deficiency in ecology knowledge, of bees and their associated fauna.Implications for insect conservation.We suggest that, besides their role in pollination, large and stable bee nesting sites increase the local insect biodiversity, and that attention should be paid to their conservation within actions aimed to support populations of wild pollinators.

Characterization and temporal patterns of red hind grouper, Epinephelus guttatus, choruses at a single aggregation site over a 10-year period

Characterization and temporal patterns of red hind grouper, Epinephelus guttatus, choruses at a single aggregation site over a 10-year period

Infrared Small Target Detection Based on a Temporally-Aware Fully Convolutional Neural Network

In the field of computer vision, the detection of infrared small targets (IRSTD) is a crucial research area that plays an important role in space exploration, infrared warning systems, and other applications. However, the existing IRSTD methods are prone to generating a higher number of false alarms and an inability to accurately locate the target, especially in scenarios with low signal-to-noise ratio or high noise interference. To address this issue, we proposes a fully convolutional-based small target detection algorithm (FCST). The algorithm builds on the anchor-free detection method FCOS and adds a focus structure and a single aggregation approach to design a lightweight feature extraction network that efficiently extracts features for small targets. Furthermore, we propose a feature refinement mechanism to emphasize the target and suppress conflicting information at multiple scales, enhancing the detection of infrared small targets. Experimental results demonstrate that the proposed algorithm achieves a detection rate of 95% and a false alarm rate of 2.32% for IRSTD tasks. To tackle even more complex scenarios, we propose a temporally-aware fully convolutional infrared small target detection (TFCST) algorithm that leverages both spatial and temporal information from sequence images. Building on a single-frame detection network, the algorithm incorporates ConvLSTM units to extract spatiotemporal contextual information from the sequence images, boosting the detection of infrared small targets. The proposed algorithm shows fast detection speed and achieves a 2.73% improvement in detection rate and an 8.13% reduction in false alarm rate relative to the baseline single-frame detection networks.

When opposites attract—Impact of cultural distance and polarised success in the future of economic unions

Aggregations between nations may be originated from many reasons: military, political, religious and especially economic. Basing ourselves on neoclassical economic theory and identity theory (i.e. local versus global), the aim of this research is to focus on international economic unions. Thus, we consider as economic polarizations, to investigate the levels of cultural homogeneity within the single polarization, to verify: i) whether in the single aggregation there is, in addition to economic harmonization, also cultural homogeneity and ii), if cultural homogeneity goes hand in hand with the economic success of the aggregation, with the help of Hofstede Model on 5 international economic unions. We also determine the crucial weight of cultural heterogeneity on the success of economic unions. The results also offer relevant theoretical and managerial implications for future aggregations.

BiTNet: A lightweight object detection network for real-time classroom behavior recognition with transformer and bi-directional pyramid network

In this paper, we propose BiTNet, a real-time object detection network, for enhancing teaching quality and providing feedback through real-time analysis of student behavior in classroom. BiTNet addresses challenges faced by current methods, such as occlusion and small objects in images. Specifically, it incorporates an Efficient Transformer Block (ETB) and Efficient Convolution Aggregation Block (ECAB) for extracting image features. ETB uses convolutional multi-head self-attention (CMHSA) to capture contextual information thus improves the capability to recoginze occluded objects. Its homogeneous multi-branch design reduces the computational costs. The single aggregation operation of ECAB aggregates the extracted features into a single feature map, improving the accuracy of recognition while reducing the computational cost. Then, the semantic and positional information extracted from the backbone are fused in Bi-directional Feature Pyramid Network (BiFPN). BiTNet also employs a smaller size detection head for recognition of small objects. Experimental results on classroom behavior dataset demonstrate that our proposed method has impressive accuracy (82.9%) and speed (256.7 FPS on GPU, 30.7 FPS on CPU, and 18.9 on camera). We further experiment with our method on Pascal VOC 2007 and VisDrone2021, validating BiTNet’s generalization and its capability to recognize small objects. In conclusion, BiTNet has a simple yet efficient network structure and feature extraction module, which reduce computational cost while maintaining recognition accuracy, making it a contender for real-time classroom behavior recognition tasks.

Dynamic Collision Oxidation Behaviors of Ag Nanoparticles on Au Nano-Disk Electrodes and Nanopore Electrodes

The dynamic collision behaviors of single nanoparticles (NPs) using nano-sized electrodes in a confined space are highly desirable to be explored for revealing the intrinsic reaction mechanism that is masked in ensemble-averaged measurements. Herein, we illustrate the collision phenomena of silver NPs (Ag NPs) with different agglomeration degrees by using a gold nanodisk electrode (NDE) and a nanopore electrode (NPE). The dynamic collision behaviors of electro-oxidation of the Ag NPs at the single particle level and aggregation state were investigated on the gold NDE and NPE. The results show that the collision/oxidation events of single Ag NPs on the NDE usually lead to diffusion of NPs into the solution after the collision; thus, the degree of oxidation is low. On NPEs, the Ag NPs can hardly escape from the pore, so it shows more multi-peak phenomena and a higher oxidation charge. However, the oxidation collision of the aggregates of Ag NPs exhibits higher peak currents, longer relaxation times, and more obvious shoulder peaks on the Au NDEs and NPEs; the single collision event usually consists of a series of 1–10 discrete “sub-events” within an interval of approximately 5 ms. Compared with a single Ag NP collision, the aggregates of Ag NPs will disperse into smaller or even individual NPs after the first few collisions and then stochastic collisions will occur repeatedly. These findings can help us deeply understand the dynamic oxidation process of metal NPs and will benefit the applications in fundamental electrochemistry, electrocatalysis, and energy-related research.

Control of Host-Matrix Morphology Enables Efficient Deep-Blue Organic Light-Emitting Devices.

Mixing a sterically bulky, electron-transporting host material into a conventional single host-guest emissive layer isdemonstratedtosuppress phase separation of the host matrix while increasing the efficiency and operational lifetime of deep-blue phosphorescent organic light-emitting diodes (PHOLEDs) with chromaticity coordinates of (0.14, 0.15). The bulky host enables homogenous mixing of the molecules comprising the emissive layer while suppressing single host aggregation; a significant loss channel of nonradiative recombination. By controlling the amorphous phase of the host-matrix morphology, the mixed-host device achieves a significant reduction in nonradiative exciton decay, resulting in 120 ± 6% increase in external quantum efficiency relative to an analogous, single-host device. In contrast to single host PHOLEDs where electrons are transported by the host and holes by the dopants, both charge carriers are conducted by the mixed host, reducing the probability of exciton annihilation, thereby doubling of the deep-blue PHOLED operational lifetime. These findings demonstrate that the host matrix morphology affects almost every aspect of PHOLED performance.

On the Cation-π capabilities of infinitene (∞). Evaluation of bonding and circular dichroism properties for Infinitene-Ag(I)n (n = 1–4) complexes from relativistic DFT calculations

The helicoidal π-conjugated structure from helicenes is able to feature cation-π bonding towards Ag(I) atoms, resulting in the variation of their inherent high optical rotation values. Recently, the characterization of infinitene ([12]infinitene) as two fused [6]helicene structures with a central crossover section suggests the extension of such coordination characteristics to large and more complex structural landscapes. Herein, we evaluated the possible cation-π features of infinitene towards one to four silver cations from density functional calculations and their role in the optical rotation of the infinity-shaped backbone. The nature of the cation-π interaction for infinitene, shows a favorable formation of single and multiple Ag(I) aggregation, with a slight distortion of the structural backbone. Hence, the formation of cation- π complexes in infinitene is plausible, suggesting the expansion of its chemistry towards the formation of metal complexes in fused helicene species.

Heterogeneous and Surface-Catalyzed Amyloid Aggregation Monitored by Spatially Resolved Fluorescence and Single Molecule Microscopy

Amyloid aggregation is associated with many diseases and may also occur in therapeutic protein formulations. Addition of co-solutes is a key strategy to modulate the stability of proteins in pharmaceutical formulations and select inhibitors for drug design in the context of diseases. However, the heterogeneous nature of this multicomponent system in terms of structures and mechanisms poses a number of challenges for the analysis of the chemical reaction. Using insulin as protein system and polysorbate 80 as co-solute, we combine a spatially resolved fluorescence approach with single molecule microscopy and machine learning methods to kinetically disentangle the different contributions from multiple species within a single aggregation experiment. We link the presence of interfaces to the degree of heterogeneity of the aggregation kinetics and retrieve the rate constants and underlying mechanisms for single aggregation events. Importantly, we report that the mechanism of inhibition of the self-assembly process depends on the details of the growth pathways of otherwise macroscopically identical species. This information can only be accessed by the analysis of single aggregate events, suggesting our method as a general tool for a comprehensive physicochemical characterization of self-assembly reactions.

DMFF-Net: A dual encoding multiscale feature fusion network for ovarian tumor segmentation.

Ovarian cancer is a serious threat to the female reproductive system. Precise segmentation of the tumor area helps the doctors to further diagnose the disease. Automatic segmentation techniques for abstracting high-quality features from images through autonomous learning of model have become a hot research topic nowadays. However, the existing methods still have the problem of poor segmentation of ovarian tumor details. To cope with this problem, a dual encoding based multiscale feature fusion network (DMFF-Net) is proposed for ovarian tumor segmentation. Firstly, a dual encoding method is proposed to extract diverse features. These two encoding paths are composed of residual blocks and single dense aggregation blocks, respectively. Secondly, a multiscale feature fusion block is proposed to generate more advanced features. This block constructs feature fusion between two encoding paths to alleviate the feature loss during deep extraction and further increase the information content of the features. Finally, coordinate attention is added to the decoding stage after the feature concatenation, which enables the decoding stage to capture the valid information accurately. The test results show that the proposed method outperforms existing medical image segmentation algorithms for segmenting lesion details. Moreover, the proposed method also performs well in two other segmentation tasks.

Age estimation algorithm based on deep learning and its application in fall detection

&lt;abstract&gt; &lt;p&gt;With the continuous development and progress of society, age estimation based on deep learning has gradually become a key link in human-computer interaction. Widely combined with other fields of application, this paper performs a gradient division of human fall behavior according to the age estimation of the human body, a complete priority detection of the key population, and a phased single aggregation backbone network VoVNetv4 was proposed for feature extraction. At the same time, the regional single aggregation module ROSA module was constructed to encapsulate the feature module regionally. The adaptive stage module was used for feature smoothing. Consistent predictions for each task were made using the CORAL framework as a classifier and tasks were divided in binary. At the same time, a gradient two-node fall detection framework combined with age estimation was designed. The detection was divided into a primary node and a secondary node. In the first-level node, the age estimation algorithm based on VoVNetv4 was used to classify the population of different age groups. A face tracking algorithm was constructed by combining the key point matrices of humans, and the body processed by OpenPose with the central coordinates of the human face. In the secondary node, human age gradient information was used to detect human falls based on the AT-MLP model. The experimental results show that compared with Resnet-34, the MAE value of the proposed method decreased by 0.41. Compared with curriculum learning and the CORAL-CNN method, MAE value decreased by 0.17 relative to the RMSE value. Compared with other methods, the method in this paper was significantly lower, with a biggest drop of 0.51.&lt;/p&gt; &lt;/abstract&gt;

Molecular Mechanism of Cyanidin-3-O-Glucoside Disassembling Aβ Fibril In Silico

The deposition of β-amyloid (Aβ) in the brain leads to neurotoxic effects and subsequent Alzheimer's disease (AD). While AD is becoming more and more prevalent in modern society, therapeutic efforts targeting Aβ could be a promising solution. Currently, two natural products are reported to disintegrate preformed Aβ fibril in vitro. Meanwhile, the chemical driving force behind this phenomenon remains unknown. Taking cyanidin-3-O-glucoside (Cy-3G) as an example, here we studied its interaction with different Aβ polymorphs in silico. Negative charges on different Aβ polymorphs draw the interaction with the flavylium cation on Cy-3G. Our results show that Aβ in a single peptide form in solution exposed more hydrophobic solvent accessible surface area than its fibril structure (per protomer), and Cy-3G interacts more intensively with the single peptide form than fibril as indicated by more hydrogen bonding formed and more amino acid residues involved in their hydrophobic interactions. Thus, the single Aβ peptide aggregation into fibril and fibril dissociation into single peptide equilibrium could be disturbed by the preferential binding of Cy-3G to the monomeric Aβ peptide, which leads to the disassembly of the pathogenic Aβ fibril. This study offers a novel perspective of Cy-3G alleviated AD syndrome beyond its dogmatic antioxidant activity.

Intermittent Demand Forecasting Using LSTM With Single and Multiple Aggregation

Intermittent demand data is data with infrequent demand with varying number of demand sizes. Intermittent demand forecasting is useful for providing inventory control decisions. It is very important to produce accurate forecasts. Based on previous research, deep learning models, especially MLP and RNN-based architectures, have not been able to provide better intermittent data forecasting results compared to traditional methods. This research will focus on analyzing the results of intermittent data forecasting using deep learning with several levels of aggregation and a combination of several levels of aggregation. In this research, the LSTM model is implemented into two traditional models that use aggregation techniques and are specifically used for intermittent data forecasting, namely ADIDA and MAPA. The result, based on tests on the six predetermined data, the LSTM model with aggregation and disaggregation is able to provide better test results than the LSTM model without aggregation and disaggregation.&#x0D;

Plasma-induced nanoparticle aggregation for stratifying COVID-19 patients according to disease severity

Stratifying patients according to disease severity has been a major hurdle during the COVID-19 pandemic. This usually requires evaluating the levels of several biomarkers, which may be cumbersome when rapid decisions are required. In this manuscript we show that a single nanoparticle aggregation test can be used to distinguish patients that require intensive care from those that have already been discharged from the intensive care unit (ICU). It consists of diluting a platelet-free plasma sample and then adding gold nanoparticles. The nanoparticles aggregate to a larger extent when the samples are obtained from a patient in the ICU. This changes the color of the colloidal suspension, which can be evaluated by measuring the pixel intensity of a photograph. Although the exact factor or combination of factors behind the different aggregation behavior is unknown, control experiments demonstrate that the presence of proteins in the samples is crucial for the test to work. Principal component analysis demonstrates that the test result is highly correlated to biomarkers of prognosis and inflammation that are commonly used to evaluate the severity of COVID-19 patients. The results shown here pave the way to develop nanoparticle aggregation assays that classify COVID-19 patients according to disease severity, which could be useful to de-escalate care safely and make a better use of hospital resources.