Characteristics Of Different Types Research Articles

Asymmetric Convolution: An Efficient and Generalized Method to Fuse Feature Maps in Multiple Vision Tasks.

Fusing features from different sources is a critical aspect of many computer vision tasks. Existing approaches can be roughly categorized as parameter-free or learnable operations. However, parameter-free modules are limited in their ability to benefit from offline learning, leading to poor performance in some challenging situations. Learnable fusing methods are often space-consuming and timeconsuming, particularly when fusing features with different shapes. To address these shortcomings, we conducted an in-depth analysis of the limitations associated with both fusion methods. Based on our findings, we propose a generalized module named Asymmetric Convolution Module (ACM). This module can learn to encode effective priors during offline training and efficiently fuse feature maps with different shapes in specific tasks. Specifically, we propose a mathematically equivalent method for replacing costly convolutions on concatenated features. This method can be widely applied to fuse feature maps across different shapes. Furthermore, distinguished from parameter-free operations that can only fuse two features of the same type, our ACM is general, flexible, and can fuse multiple features of different types. To demonstrate the generality and efficiency of ACM, we integrate it into several state-of-the-art models on three representative vision tasks: visual object tracking, referring video object segmentation, and monocular 3D object detection. Extensive experimental results on three tasks and several datasets demonstrate that our new module can bring significant improvements and noteworthy efficiency.

MulTFBS: A Spatial-Temporal Network with Multichannels for Predicting Transcription Factor Binding Sites.

Revealing the mechanisms that influence transcription factor binding specificity is the key to understanding gene regulation. In previous studies, DNA double helix structure and one-hot embedding have been used successfully to design computational methods for predicting transcription factor binding sites (TFBSs). However, DNA sequence as a kind of biological language, the method of word embedding representation in natural language processing, has not been considered properly in TFBS prediction models. In our work, we integrate different types of features of DNA sequence to design a multichanneled deep learning framework, namely MulTFBS, in which independent one-hot encoding, word embedding encoding, which can incorporate contextual information and extract the global features of the sequences, and double helix three-dimensional structural features have been trained in different channels. To extract sequence high-level information effectively, in our deep learning framework, we select the spatial-temporal network by combining convolutional neural networks and bidirectional long short-term memory networks with attention mechanism. Compared with six state-of-the-art methods on 66 universal protein-binding microarray data sets of different transcription factors, MulTFBS performs best on all data sets in the regression tasks, with the average R2 of 0.698 and the average PCC of 0.833, which are 5.4% and 3.2% higher, respectively, than the suboptimal method CRPTS. In addition, we evaluate the classification performance of MulTFBS for distinguishing bound or unbound regions on TF ChIP-seq data. The results show that our framework also performs well in the TFBS classification tasks.

Experimental study on the damage characteristics of cyclic disturbance and acoustic emission characteristics of different types of sandstones under high stress in deep mines

AbstractThree sandstone specimens common in rock engineering were selected to study the differences in the mechanical properties of rocks with different lithologies. The development and expansion of the internal cracks in the specimens were observed by combining the simulation system with the acoustic emission system. Through the combination of dynamic and static stresses, the deformation and damage of rocks under deep rock excavation and blasting were simulated. As the results show, the acoustic emission events of specimens with different lithologies under combined static and dynamic cyclic loading can be roughly divided into three phases: weakening, stabilizing, and surging periods. In addition, the acoustic emission characteristics of specimens with different lithologies show general consistency in different compression phases. The degree of fragmentation of specimens increases with the applied stress level; therefore, the stress level is one of the important factors influencing the damage pattern of specimens. The acoustic emission system was used to simulate the deformation and damage of rocks subjected to deep rock body excavation and engineering blasting. Cyclic dynamic perturbations under sinusoidal waves with a frequency of 5 Hz, a loading rate of 0.1 mm/min, a cyclic amplitude of 5 MPa, and a loading rate of 0.1 mm/min were applied to the three rock samples during the experiments. Among them, the fine‐grained sandstones are the most sensitive to the sinusoidal cyclic perturbation, followed by the muddy siltstone and the medium‐grained sandstones. On this basis, the acoustic emission energy release characteristics were analyzed, and the waveform characteristics in the damage evolution of the specimen under dynamic perturbation were studied by extracting the key points and searching for the main frequency eigenvalues.

DSFE: Decoding EEG-based finger motor imagery using feature-dependent frequency, feature fusion and ensemble learning.

Accurate decoding finger motor imagery is essential for fine motor control using EEG signals. However, decoding finger motor imagery is particularly challenging compared with ordinary motor imagery. This paper proposed a novel EEG decoding method of featuredependent frequency band selection, feature fusion, and ensemble learning (DSFE) for finger motor imagery. First, a feature-dependent frequency band selection method based on correlation coefficient (FDCC) was proposed to select feature-specific effective bands. Second, a feature fusion method was proposed to fuse different types of candidate features to produce multiple refined sets of decoding features. Finally, an ensemble model using the weighted voting strategy was proposed to make full use of these diverse sets of final features. The results on a public EEG dataset of five fingers motor imagery showed that the DSFE method is effective and achieves the highest decoding accuracy of 50.64%, which is 7.64% higher than existing studies using exactly the same data. The experiments further revealed that both the effective frequency bands of different subjects and the effective frequency bands of different types of features are different in finger motor imagery. Furthermore, compared with two-hand motor imagery, the effective decoding information of finger motor imagery is transferred to the lower frequency. The idea and findings in this paper provide a valuable perspective for understanding fine motor imagery in-depth.

Analysis of the prenatal ultrasound diagnostic value and prognostic factors of fetal mediastinal cysts.

A prenatal fetal mediastinal cyst is a benign disease. However, if a cyst enlargement grows, it may compress the adjacent organs and affect the fetal cardiopulmonary function. This study aimed to compare and analyze the prenatal ultrasound characteristics of different mediastinal cysts, and to evaluate the pregnancy outcome of the fetus and the factors affecting the prognostic of the fetus. To compare and analyze the prenatal ultrasound characteristics of different types of mediastinal cysts, and to evaluate the fetal pregnancy outcome and the influencing factors of fetal prognosis. A retrospective analysis of patients with prenatal diagnoses of mediastinal cysts was conducted to evaluate the ultrasound characteristics and to monitor the pregnancy outcomes to identify prognostic influences and provide a reliable basis for patient prognosis. In total, 30 patients were diagnosed with mediastinal cysts [including bronchogenic cysts (n=12), esophageal cysts (n=9), pericardial cysts (n=5), and thymic cysts (n=4)] on prenatal ultrasonography. The diagnostic accuracy rate was 93.33%; two cases of esophageal cysts were misdiagnosed as bronchial cysts. In total, 4 (44.44%) of 9 esophageal cysts and 4 thymic cysts were located in the anterior mediastinum, 10 (83.33%) of 12 bronchogenic cysts and 5 pericardial cysts were located in the middle mediastinum, and 2 (16.67%) of 12 bronchogenic cysts and 5 (55.56%) of 9 esophageal cysts were located in the posterior mediastinum. There were significant differences in the distribution of the cyst location, morphology, and cyst wall thickness (P<0.05). After delivery, 17 patients had clinical symptoms. There was a significant difference in the clinical symptoms between patients with a maximum diameter of postpartum cysts <5 and ≥5 cm (P<0.05), and children with a low gestational age and birth weight were more likely to have clinical symptoms. The prenatal ultrasound features of fetal mediastinal cysts were similar. However, the ultrasound characteristics related to the cyst location, morphology, and cyst wall thickness were helpful in providing an accurate diagnosis. In addition, the postpartum cyst size, location, adjacent relationship with the surrounding tissues, volume, gestational age, and weight were related to patient prognosis.

Pseudo-Helmholtz decomposition for an elastic VTI wavefield based on wavefront phase direction

P and S waves are coupled when propagating in anisotropic elastic media. The separation of P and S waves helps to study the characteristics of different types of seismic waves as well as mitigating crosstalk artifacts in elastic reverse time migration and elastic full-waveform inversion. At present, the methods of seismic wave mode separation in anisotropic media are mainly built on divergence- and curl-like operations, pseudo-Helmholtz decomposition, and low-rank approximation. We develop a new pseudo-Helmholtz decomposition operator based on eigenform analysis and the wavefront phase direction to decompose vertically transversely isotropic elastic wavefields. The corresponding P-/S-wave decoupling formulas are also derived in detail. Compared with the divergence- and curl-like methods, the new method does not change the phase of P and S waves. Compared with existing pseudo-Helmholtz decomposition methods based on eigenform analysis, our method achieves more accurate wavefield separation than the zero-order pseudo-Helmholtz decomposition operator. Our method requires solving one vector Poisson equation only, resulting in much less computational cost than the existing first-order pseudo-Helmholtz decomposition methods. In addition, the accuracy of our method is analyzed by providing homogeneous media with different parameter settings. Finally, the numerical examples demonstrate that the new pseudo-Helmholtz decomposition method is effective, efficient, and robust against random noise.

Feature Importance Analysis and Machine Learning for Alzheimer's Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio.

Alzheimer's disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15-20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images. To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs' superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category. Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer's detection.

Characterisation of different types of power plant ashes as potential lunar regolith simulants

Characterisation of different types of power plant ashes as potential lunar regolith simulants

A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis

Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dynamic features extracted from multichannel electroencephalography (EEG) signals when listeners attend to a target speaker in the presence of a competing talker. To this aim, microstate and recurrence quantification analysis are utilized to extract different types of features that reflect changes in the brain state during cognitive tasks. Then, an optimized feature set is determined by employing the processes of significant feature selection based on classification performance. The classifier model is developed by hybrid sequential learning that employs Gated Recurrent Units (GRU) and Convolutional Neural Network (CNN) into a unified framework for accurate attention detection. The proposed AAD method shows that the selected feature set achieves the most discriminative features for the classification process. Also, it yields the best performance as compared with state-of-the-art AAD approaches from the literature in terms of various measures. The current study is the first to validate the use of microstate and recurrence quantification parameters to differentiate auditory attention using reinforcement learning without access to stimuli.

The covariant structural and functional neuro-correlates of cognitive impairments in patients with end-stage renal diseases.

Cognitive impairment (CI) is a common complication of end-stage renal disease (ESRD) that is associated with structural and functional changes in the brain. However, whether a joint structural and functional alteration pattern exists that is related to CI in ESRD is unclear. In this study, instead of looking at brain structure and function separately, we aim to investigate the covariant characteristics of both functional and structural aspects. Specifically, we took the fusion analysis approach, namely, multimodal canonical correlation analysis and joint independent component analysis (mCCA+jICA), to jointly study the discriminative features in gray matter volume (GMV) measured by T1-weighted (T1w) MRI, fractional anisotropy (FA) in white matter measured by diffusion MRI, and the amplitude of low-frequency fluctuation (ALFF) measured by blood oxygenation-level-dependent (BOLD) MRI in 78 ESRD patients versus 64 healthy controls (HCs), followed by a mediation effect analysis to explore the relationship between neuroimaging findings, cognitive impairments and uremic toxins. Two joint group-discriminative independent components (ICs) were found to show covariant abnormalities across FA, GMV, and ALFF (all p < 0.05). The most dominant joint IC revealed associative patterns of alterations of GMV (in the precentral gyrus, occipital lobe, temporal lobe, parahippocampal gyrus, and hippocampus), alterations of ALFF (in the precuneus, superior parietal gyrus, and superior occipital gyrus), and of white matter FA (in the corticospinal tract and inferior frontal occipital fasciculus). Another significant IC revealed associative alterations of GMV (in the dorsolateral prefrontal and orbitofrontal cortex) and FA (in the forceps minor). Moreover, the brain changes identified by FA and GMV in the above-mentioned brain regions were found to mediate the negative correlation between serum phosphate and mini-mental state examination (MMSE) scores (all p < 0.05). The mCCA+jICA method was demonstrated to be capable of revealing covariant abnormalities across neuronal features of different types in ESRD patients as contrasted to HCs, and joint brain changes may play an important role in mediating the relationship between serum toxins and CIs in ESRD. Our results show the mCCA+jICA fusion analysis approach may provide new insights into similar neurobiological studies.

Research on the Classification of Rail Transit Stations and Passenger Flow Patterns—A Case from Xi’an, China

Transit-oriented development (TOD) has been promoted and implemented worldwide through the efficient integration of rail transit stations and land use. However, the interactions between stations and the surrounding catchment areas (CAs) are characterized by different features of the built environment and regional development. Therefore, it is necessary to develop a quantitative classification method for rail transit stations based on the built environment within a CA and to identify the passenger flow characteristics of different types of stations to develop targeted planning and design policies. In this study, Line 1 and Line 2 within the third ring road of Xi’an City were taken as the objects, and a station classification system was constructed by taking station traffic levels and different building functions within the CA as the classification factors. Secondly, indicators of the built environment, such as six different types of functions, were calculated through refined vector modeling, and 30 typical stations were typologically analyzed. Furthermore, 10 typical types—totaling 11 stations—were selected for passenger flow monitoring, and the passenger flow characteristics of the different types of stations were summarized in terms of the dimensions of stations and exits. Finally, the correlations between the indicators of the built environment and passenger traffic for different functions were quantified. This study provides a basis for the future optimization of stations and the built environment, as well as station design and management.

Morphometric Features of Different Types of Bifurcations in the Splenic Intraorgan Arterial System in Individuals of Different Gender and Age

INTRODUCTION: A promising direction that has recently emerged in morphology is investigation of the arterial bed of various human organs as fractal or quasi-fractal systems. Conceptual models have been developed permitting a quantitative description of the vascular bed features. This approach will help to create a morphometric standard of the intraorgan blood flow, which will be useful in objective diagnosis of probable deviations from the normal structure.&#x0D; AIM: To identify the morphometric features of various types of bifurcations of the splenic intraorgan arterial bed in individuals of different gender and age.&#x0D; MATERIALS AND METHODS: The characteristics of the splenic intraorgan arterial bed have been studied in 67 individuals who died suddenly and from accidental causes at the age of 21 to 60 years. The arterial bed was represented as connected graphs with vertices corresponding to the bifurcation points of arteries, and edges to the arterial segments. The diameter and length of an arterial segment were measured on corrosion preparations. Based on the morphometry data, the following parameters were determined: generation number, division level, form factor FF1, branching factor ƞ and asymmetry factor γ. Statistical analysis was carried out using the R language.&#x0D; RESULTS: The total number of examined arterial bifurcations was 6,840. The examined bifurcations were located at 20 division levels and presented 8 generations. In the structure of the vascular bed, bifurcations of neutral kind (0) predominated with the relative quantity 51%. The least numerous was type 2 bifurcations — 9%. The intermediate position was taken by open (1) bifurcations accounting for about 40% in the vascular bed structure.&#x0D; CONCLUSIONS: The intraorgan arterial bed of the spleen is a quasi-fractal system consisting of three types of bifurcations — open, neutral and closed. A relative number of different types of bifurcations differs depending on gender and age and is also related to the generation number and division level.

Hydrochemical characteristics of different types of lakes in the Volzhsko-Kamsky Reserve

Hydrochemical characteristics of different types of lakes in the Volzhsko-Kamsky Reserve

The developmental trajectory of college students’ psychological flexibility: Based on latent growth model

The purpose of this study is to explore the development law of college students' psychological flexibility (PF), the influencing factors of demographic variables, and group heterogeneity through longitudinal tracking. This study collected the PF data of 1068 college students in three times and constructed the data model. Results show that the PF level of college students exhibited a significant upward trend across the three measurements, gender and left-behind experience had a remarkable impact on the slope factor. It means the PF of college students improves over time, gender and left-behind experience may affect the speed and amplitude of the improvement. The development trajectory of PF of college students showed obvious group heterogeneity and could be divided into the “high flexibility-low development group” (92%) and the “low flexibility-high development group” (8%). It means college students can be divided into two categories, which allows for the implementation of tailored psychological counseling and intervention methods. The practical significance of this study is that it has a certain grasp of the development track and law of college students' PF, which is helpful for mental health educators to teach students by their aptitude. According to the development characteristics of different types of college students' PF, different methods of psychological counseling and intervention are adopted to improve the effectiveness of mental health education.

Revolutionizing energy practices: Unleashing the power of artificial intelligence in corporate energy transition

Corporate energy transition is crucial for long-term sustainable development. The widely discussed Artificial Intelligence (AI), as a disruptive technological innovation, is highly potential for enhancing environment performance. However, the specific impact of AI on the process of corporate energy transition and its underlying mechanisms have not been fully explored. This study focuses on A-share listed corporates in Shanghai and Shenzhen stock markets in China spanning from 2011 to 2021. Based on corporate annual report information and information from over 200,000 patent application texts, we innovatively construct indicators for corporate energy transition and AI technology application. Furthermore, we empirically investigate the impact of AI technology on corporate energy transition and its potential mechanisms through combining information asymmetry theory and institutional theory. The empirical results indicate that: 1) AI can drive corporate energy transition and the promoting effect of AI collaborative innovation on corporate energy transition should not be ignored. 2) AI can help corporates achieve energy transition through pathways such as mitigating information asymmetry, reducing financing constraints, adjusting sustainable development concepts and practices. 3) The driving effect of AI on corporate energy transition varies depending on the characteristics of different types of corporates, industries, and regions. This study provides strategic guidance and decision support for business managers and policymakers, assisting both corporates and governments in better utilizing AI technology during the social energy transition process to achieve a dual optimization of environmental and economic goals.

Corporate identity and rebranding of the student council of the Higher school of folk arts

The article is devoted to the development of the corporate identity of the infographics of the student government of the Higher School of Folk Arts (Academy). The characteristics of different types of logos are analyzed, their weaknesses and strengths are identified; a set of elements and colors for the logo of the student council and the logos of the student council committees are developed. Style bearers are being identified, souvenir projects are being developed. Special attention is paid to the design of the final version of the logo of the Student Council of the Higher School of Folk Arts (Academy), the analysis of factors affecting the visual perception of logos and their application in the student environment.

CLINICAL AND RADIOGRAPHIC FEATURES OF ODONTOGENIC CYSTS OF JAW PRESENTED TO THE DEPARTMENT OF ORAL AND MAXILLOFACIAL SURGERY, KHYBER COLLEGE OF DENTISTRY PESHAWAR

Objectives: To determine frequency, clinical and radiographic features of diff erent types of odontogenic cysts in patients reported to the department of Oral and Maxillofacial surgery, Khyber College of Dentistry Peshawar.Materials and Methods: This descriptive, cross sectional study was conducted at department of Oral and Maxillofacial Surgery, Khyber College of Dentistry, Peshawar from November 2019 to October 2021.This study was conducted on 89 patients with histopathologically confi rmed reports of odontogenic cysts. Data with respect to age, gender, type of odontogenic cyst, clinical and radiographic features of these cysts were analyzed from the data obtained through a customized structured proforma.Results: Out of the total 89 patient, there was male predilection (2.17:1). The most frequent age group was 2nd and 3rd decades. Among diff erent types of odontogenic cysts, radicular cyst (37.1%) was the most common cyst followed by dentigerous cyst (33.7%) and odontogenic keratocyst (27%). The most common aff ected site was posterior mandible (46.1%). Paresthesia was present in 22.5% cases. Unicortical expansion was present in 59.6 % and bicortical expansion in 38.2% cases. Tenderness was present in 50.6% cases and teeth mobility in 28.1% cases. Needle aspiration test was positive in 78.7% cases. Mean size was 32.23mm + 14.53 standard deviation. Unilocular radiolucency was present in 74.2% and multilocular radiolucency in 25.8%. Root resorption was present in 64% cases and impacted tooth was associated with 52.2% odontogenic cysts.Conclusion: It is concluded from this study that odontogenic cysts frequently encountered during 2nd and 3rd decades of life and more common in males with a predilection for the posterior mandible. Radicular cysts are most frequent among the other types of odontogenic cysts. Majority of patients has cortical expansion and positive needle aspiration test. Impacted tooth is associated with all the dentigerous cysts and most of OKC‘s and none of radicular cyst. Odontogenic cysts can reach to a considerable size to involve even half of the mandible.

Biomaterial-Based CRISPR/Cas9 Delivery Systems for Tumor Treatment.

CRISPR/Cas9 gene editing technology is characterized by high specificity and efficiency, and has been applied to the treatment of human diseases, especially tumors involving multiple genetic modifications. However, the clinical application of CRISPR/Cas9 still faces some major challenges, the most urgent of which is the development of optimized delivery vectors. Biomaterials are currently the best choice for use in CRISPR/Cas9 delivery vectors owing to their tunability, biocompatibility, and efficiency. As research on biomaterial vectors continues to progress, hope for the application of the CRISPR/Cas9 system for clinical oncology therapy builds. In this review, we first detail the CRISPR/Cas9 system and its potential applications in tumor therapy. Then, we introduce the different delivery forms and compare the physical, viral, and non-viral vectors. In addition, we analyze the characteristics of different types of biomaterial vectors. We further review recent research progress in the use of biomaterials as vectors for CRISPR/Cas9 delivery to treat specific tumors. Finally, we summarize the shortcomings and prospects of biomaterial-based CRISPR/Cas9 delivery systems.

Вопросы организации экспертизы модели угроз

Currently, there is a need to develop a threat model regulated by a number of regulatory documents for various information systems that are subject to protection in accordance with current legislation. The threat model itself is a document whose main purpose is to identify current threats to a particular system. However, there are different types of information systems, for each of which the task of compiling a threat model may have different resource intensity. Also, an important aspect is the correctness of the developed threat model, since there is a law on liability for offenses in the field of information protection. Based on this, there is a need to consider the features of building a threat model for different types of information systems in order to simplify its examination. The paper presents a structured algorithm for building a threat model with features for different types of information systems.

Thiophene-Based Ligands for Specific Assignment of Distinct Aβ Pathologies in Alzheimer's Disease.

Aggregated species of amyloid-β (Aβ) are one of the pathological hallmarks in Alzheimer's disease (AD), and ligands that selectively target different Aβ deposits are of great interest. In this study, fluorescent thiophene-based ligands have been used to illustrate the features of different types of Aβ deposits found in AD brain tissue. A dual-staining protocol based on two ligands, HS-276 and LL-1, with different photophysical and binding properties, was developed and applied on brain tissue sections from patients affected by sporadic AD or familial AD associated with the PSEN1 A431E mutation. When binding to Aβ deposits, the ligands could easily be distinguished for their different fluorescence, and distinct staining patterns were revealed for these two types of AD. In sporadic AD, HS-276 consistently labeled all immunopositive Aβ plaques, whereas LL-1 mainly stained cored and neuritic Aβ deposits. In the PSEN1 A431E cases, each ligand was binding to specific types of Aβ plaques. The ligand-labeled Aβ deposits were localized in distinct cortical layers, and a laminar staining pattern could be seen. Biochemical characterization of the Aβ aggregates in the individual layers also showed that the variation of ligand binding properties was associated with certain Aβ peptide signatures. For the PSEN1 A431E cases, it was concluded that LL-1 was binding to cotton wool plaques, whereas HS-276 mainly stained diffuse Aβ deposits. Overall, our findings showed that a combination of ligands was essential to identify distinct aggregated Aβ species associated with different forms of AD.