Density-based Analysis Research Articles

Clustered remote sensing target distribution detection aided by density-based spatial analysis

Small target detection in remote sensing is integral to a range of applications, including smart city systems and emergency rescue operations. However, the challenges posed by weak features and complex backgrounds in remote sensing images have hindered the efficacy of detection. Current models tend to focus on identifying individual targets, resulting in algorithms with larger parameters, slower detection efficiency, and difficulty in striking a balance between false positives and negatives. Given that many tasks do not require precise target location, a more efficient approach involves swiftly predicting target areas with models involving fewer parameters. This paper introduces the concept of group target distribution detection, gathering targets with similar distances and semantic similarities for clustered detection. A Gaussian probability map, formed from target density, is used to train a probability prediction model. We propose a new metric for evaluating this innovative group target distribution detection paradigm and provide a comparative assessment with traditional single-object detection models. In experimental evaluation, our proposed DenseUGE network — employing ResNet34 and ResNet50 as its backbone — surpasses the best baseline method by 3.37% on the AI-TOD dataset using our metrics. Additionally, visualizations demonstrate the ability of our proposed methodology to effectively identify the concentrated distribution of small target groups.

Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI).

Magnetic resonance imaging (MRI) is a primary non-invasive imaging modality for tumor segmentation, leveraging its exceptional soft tissue contrast and high resolution. Current segmentation methods typically focus on structural MRI, such as T1-weighted post-contrast-enhanced or fluid-attenuated inversion recovery (FLAIR) sequences. However, these methods overlook the blood perfusion and hemodynamic properties of tumors, readily derived from dynamic susceptibility contrast (DSC) enhanced MRI. This study introduces a novel hybrid method combining density-based analysis of hemodynamic properties in time-dependent perfusion imaging with deep learning spatial segmentation techniques to enhance tumor segmentation. First, a U-Net convolutional neural network (CNN) is employed on structural images to delineate a region of interest (ROI). Subsequently, Hierarchical Density-Based Scans (HDBScan) are employed within the ROI to augment segmentation by exploring intratumoral hemodynamic heterogeneity through the investigation of tumor time course profiles unveiled in DSC MRI. The approach was tested and evaluated using a cohort of 513 patients from the open-source University of Pennsylvania glioblastoma database (UPENN-GBM) dataset, achieving a 74.83% Intersection over Union (IoU) score when compared to structural-only segmentation. The algorithm also exhibited increased precision and localized predictions of heightened segmentation boundary complexity, resulting in a 146.92% increase in contour complexity (ICC) compared to the reference standard provided by the UPENN-GBM dataset. Importantly, segmenting tumors with the developed new approach uncovered a negative correlation of the tumor volume with the scores in the Karnofsky Performance Scale (KPS) clinically used for assessing the functional status of patients (-0.309), which is not observed with the prevailing segmentation standard. This work demonstrated that including hemodynamic properties of tissues from DSC MRI can improve existing structural or morphological feature-based tumor segmentation techniques with additional information on tumor biology and physiology. This approach can also be applied to other clinical indications that use perfusion MRI for diagnosis or treatment monitoring.

Magnetic levitation of nanoscale materials: the critical role of effective density

The magnetic levitation (MagLev) of diamagnetic materials in a paramagnetic solution is a robust technique for the density-based separation, measurements, and analysis of bulk materials/objects (e.g., beads and plastics). There is a debate in the literature, however, about whether a MagLev technique is reliable for the separation and/or density measurements of nanoscale objects. Here, we show that MagLev can levitate nanoparticles; however, the transition from the bulk to an ‘effective’ density must be acknowledged and considered in density measurements at the nanoscale regime. We performed a proof-of-concept study on MagLev’s capability in measuring the ‘effective density’ of multiscale silver particles (i.e. microparticles, nanopowder, and nanoemulsion). In addition, we probed the effective density of nanoscale biomolecules (e.g. lipoproteins) using a standard MagLev system. Our findings reveal that the MagLev technique has the capability to measure both bulk density (which is independent of the size and dimension of the material) and the effective density (which takes place at the nanoscale regime and is dependent on the size and surrounding paramagnetic solution) of the levitated objects.

Asymmetric magnetic levitation for density-based measurement and analysis

BackgroundMagnetic levitation (MagLev) based on negative magnetophoresis represents a promising technology for density-based analysis and manipulation of nonmagnetic objects. This approach has garnered considerable interest across multiple fields, such as chemistry, materials science, and biochemistry, primarily due to its inherent simplicity, precision, and cost-effectiveness. However, it is essential to recognize that frequently used MagLev configurations, including standard MagLev and axial MagLev, are not without their limitations. These configurations often struggle to strike a balance between levitation performance, ease of operation, and visibility. Therefore, it is necessary to develop a new MagLev configuration to address the aforementioned issue. ResultsThis work describes the development of an innovative MagLev, termed “asymmetric MagLev”, achieved by combining a ring magnet and a cylinder magnet as up-down asymmetric magnetic field sources. The asymmetric design overcomes the physical obstacles along the centerline of the standard MagLev, offering unique open-structure advantages, including easy handling of samples, the ability to observe samples from the top or bottom, and no restrictions on the container height. Meanwhile, comparative analysis reveals a considerable enhancement in the working distance of the asymmetric MagLev without significantly sacrificing the measurement range compared to the axial MagLev. Notably, the asymmetric MagLev achieves a remarkable sensitivity of up to about 1.8 × 104 mm (g cm−3)−1, surpassing the axial MagLev by approximately 30 times. Furthermore, experimental results validate the successful application of the asymmetric MagLev in density measurement and quality detection of small-sized objects. SignificanceThis pioneering configuration represents the first utilization of up-down asymmetric magnets in the field of MagLev. Through the integration of an axially magnetized ring magnet and a cylinder magnet, the asymmetric MagLev design overcomes the limitations associated with conventional MagLev configurations. This innovative design exhibits outstanding operational capabilities and levitation performance, making it suitable for a wide range of applications in density-based measurement and analysis.

Unsupervised Cluster Analysis of Walking Activity Data for Healthy Individuals and Individuals with Lower Limb Amputation.

This is the first investigation to perform an unsupervised cluster analysis of activities performed by individuals with lower limb amputation (ILLAs) and individuals without gait impairment, in free-living conditions. Eight individuals with no gait impairments and four ILLAs wore a thigh-based accelerometer and walked on an improvised route across a variety of terrains in the vicinity of their homes. Their physical activity data were clustered to extract 'unique' groupings in a low-dimension feature space in an unsupervised learning approach, and an algorithm was created to automatically distinguish such activities. After testing three dimensionality reduction methods-namely, principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approximation and projection (UMAP)-we selected tSNE due to its performance and stable outputs. Cluster formation of activities via DBSCAN only occurred after the data were reduced to two dimensions via tSNE and contained only samples for a single individual. Additionally, through analysis of the t-SNE plots, appreciable clusters in walking-based activities were only apparent with ground walking and stair ambulation. Through a combination of density-based clustering and analysis of cluster distance and density, a novel algorithm inspired by the t-SNE plots, resulting in three proposed and validated hypotheses, was able to identify cluster formations that arose from ground walking and stair ambulation. Low dimensional clustering of activities has thus been found feasible when analyzing individual sets of data and can currently recognize stair and ground walking ambulation.

Assessing safety functionalities in the design and validation of driving automation

This paper aims to contribute to the comprehensive and systematic safety assessment of Automated Driving Systems (ADSs) by identifying unknown hazardous areas of operation. The current methodologies employed in this domain typically involve estimating the distributions of situational variables based on human-centered field test, crash databases, or expert knowledge of critical values. However, due to the lack of a-priori knowledge regarding the influential factors, their critical ranges, and their distributions, these approaches may not be entirely suitable for the assessment of emerging automated driving technologies. To deal with this challenging problem, here we propose a testing methodology incorporating realistic yet unobserved driving conditions, distinguished by numerous situational variables, so to encompass unknown unsafe conditions comprehensively. Our methodology utilizes stochastic simulation and uncertainty modeling techniques to account for the variability of realistic driving conditions and their impact on ADSs' performances. By doing so, we aim to identify unsafe operational regions and triggering conditions that can lead to hazardous behaviors, thus improving the development and safety of automated driving functions. For our purposes, the Latin Hypercube Sampling technique and the recently proposed PAWN density-based sensitivity analysis method are employed. We apply this methodology for the first time in the specific field of ADSs design and validation, using an exemplificative use case. We discuss and compare the results obtained from our approach with those obtained from a traditional approach.

Dynamically Rotating Magnetic Levitation to Characterize the Spatial Density Heterogeneity of Materials.

Magnetic levitation (MagLev) is a promising technology for density-based analysis and manipulation of nonmagnetic materials. One major limitation is that extant MagLev methods are based on the static balance of gravitational-magnetic forces, thereby leading to an inability to resolve interior differences in density. Here a new strategy called "dynamically rotating MagLev" is proposed, which combines centrifugal force and nonlinear magnetic force to amplify the interior differences in density. The design of the nonlinear magnetic force in tandem with centrifugal force supports the regulation of stable equilibriums, enabling different homogeneous objects to reach distinguishable equilibrium orientations. Without reducing the magnetic susceptibility, the dynamically rotating MagLev system can lead to a relatively large change in orientation angle (∆ψ>50°) for the heterogeneous parts with small inclusions (volume fraction VF = 2.08%). The rich equilibrium states of levitating objects invoke the concept of levitation stability, which is employed, for the first time, to characterize the spatial density heterogeneity of objects. Exploiting the tunable nonlinear levitation behaviors of objects provides a new paradigm for developing operationally simple, nondestructive density heterogeneity characterization methods. Such methods have tremendous potential in applications related to sorting, orienting, and assembling objects in three dimensions.

Packing Density-Based Rheological Analysis of Self-consolidating Concrete Containing Mineral and Chemical Admixtures

Packing Density-Based Rheological Analysis of Self-consolidating Concrete Containing Mineral and Chemical Admixtures

Bonding and antibonding characters of the first order reduced density matrix. A new look at two charge-shift bonds.

This article reports a new way of analyzing chemical bonds based on the use of gradient of a first order reduced density matrix (1-RDM) map to partition bonds into atomic core, valence, and dorsal regions that can be further characterized as bonding or antibonding. These classifications are directly related to the familiar language of molecular orbital analysis. Analyses based on these quantities and the critical points of the 1-RDM performed on two charge-shift bonds show that although they share common features, there are fundamental differences in these interactions that are detected neither by density-based analysis nor by valence bond theory. The results suggest the gradient of 1-RDM as a valuable tool in bond analysis.

Spatial Analysis of NQO1 in Non-Small Cell Lung Cancer Shows Its Expression Is Independent of NRF1 and NRF2 in the Tumor Microenvironment.

Nuclear factor erythroid 2-related factor 1 (NFE2L1, NRF1) and nuclear factor erythroid 2-related factor 2 (NFE2L2, NRF2) are distinct oxidative stress response transcription factors, both of which have been shown to perform cytoprotective functions, modulating cell stress response and homeostasis. NAD(P)H:quinone oxidoreductase (NQO1) is a mutual downstream antioxidant gene target that catalyzes the two-electron reduction of an array of substrates, protecting against reactive oxygen species (ROS) generation. NQO1 is upregulated in non-small cell lung cancer (NSCLC) and is proposed as a predictive biomarker and therapeutic target. Antioxidant protein expression of immune cells within the NSCLC tumor microenvironment (TME) remains undetermined and may affect immune cell effector functions and survival outcomes. Multiplex immunofluorescence was performed to examine the co-localization of NQO1, NRF1 and NRF2 within the tumor and TME of 162 chemotherapy-naïve, early-stage NSCLC patients treated by primary surgical resection. This study demonstrates that NQO1 protein expression is high in normal, tumor-adjacent tissue and that NQO1 expression varies depending on the cell type. Inter and intra-patient heterogenous NQO1 expression was observed in lung cancer. Co-expression analysis showed NQO1 is independent of NRF1 and NRF2 in tumors. Density-based co-expression analysis demonstrated NRF1 and NRF2 double-positive expression in cancer cells is associated with improved overall survival.

Using machine learning to identify gene interaction networks associated with breast cancer

BackgroundBreast cancer (BC) is one of the most prevalent cancers worldwide but its etiology remains unclear. Obesity is recognized as a risk factor for BC, and many obesity-related genes may be involved in its occurrence and development. Research assessing the complex genetic mechanisms of BC should not only consider the effect of a single gene on the disease, but also focus on the interaction between genes. This study sought to construct a gene interaction network to identify potential pathogenic BC genes.MethodsThe study included 953 BC patients and 963 control individuals. Chi-square analysis was used to assess the correlation between demographic characteristics and BC. The joint density-based non-parametric differential interaction network analysis and classification (JDINAC) was used to build a BC gene interaction network using single nucleotide polymorphisms (SNP). The odds ratio (OR) and 95% confidence interval (95% CI) of hub gene SNPs were evaluated using a logistic regression model. To assess reliability, the hub genes were quantified by edgeR program using BC RNA-seq data from The Cancer Genome Atlas (TCGA) and identical edges were verified by logistic regression using UK Biobank datasets. Go and KEGG enrichment analysis were used to explore the biological functions of interactive genes.ResultsBody mass index (BMI) and menopause are important risk factors for BC. After adjusting for potential confounding factors, the BC gene interaction network was identified using JDINAC. LEP, LEPR, XRCC6, and RETN were identified as hub genes and both hub genes and edges were verified. LEPR genetic polymorphisms (rs1137101 and rs4655555) were also significantly associated with BC. Enrichment analysis showed that the identified genes were mainly involved in energy regulation and fat-related signaling pathways.ConclusionWe explored the interaction network of genes derived from SNP data in BC progression. Gene interaction networks provide new insight into the underlying mechanisms of BC.

3D cumulus cloud scene modelling and shadow analysis method based on ground-based sky images

Cloud scene modelling and shadow analysis in 3D geographical scenes can provide auxiliary support for evaluating photovoltaic (PV) power generation. Thus far, real cloud scene based on measured data has not been constructed in 3D geographical scenes, and shadow with different intensity caused by cumulus cloud thickness has not been considered. In this study, a 3D cumulus cloud scene modelling and shadow analysis method based on ground-based sky images was proposed. First, a voxel-based 3D cumulus cloud scene modelling on GPU was proposed to model in the 3D geographic space and calculate cumulus cloud density. Then, a cumulus cloud density-based shadow analysis method was proposed, which can express shadow intensity. The experimental result showed that our method can accomplish near real-time 3D cumulus cloud modelling and shadow analysis in a 3D geographic space based on ground-based sky images, and the shadow intensity is inversely proportional to the direct solar radiation.

Sensitive and rapid protein assay via magnetic levitation

Magnetic levitation (MagLev) is a newly emerging methodology for biosensing that provides a density-based analysis, which is highly sensitive and versatile. In this study, a magnetic levitation based sensor platform was used for protein detection; and sensor platform optimization was performed for both sensitivity and resolution. Bovine Serum Albumin (BSA) was used as a model protein and detection of BSA was carried out by antibody functionalized polystyrene microspheres (PSMs). Various sizes of PSMs were examined and their performances were compared by statistical analyses in terms of limit of detection (LOD), sensitivity, and resolution. Quantification of the protein was done based on the magnetic levitation height differences of antibody functionalized PSMs. For optimization of the methodology, varied PSMs were utilized, and standardization of PSM diameter, concentration of the antibody to be functionalized, and PSM dilution rates were carried out. In conclusion, 20 μm PSMs diluted to 0.005% W/V and functionalized with anti-BSA antibody at a concentration of 28 μg/ml were determined to provide the best resolution for BSA detection. A dynamic range of 100 nM to 1 mM was observed with an LOD value of 4.1 ng/ml. This sensing platform promises a novel approach with a diverse application field and it provides rapid, consistent, and reproducible results with high resolution and sensitivity.

Pump-free microfluidic magnetic levitation approach for density-based cell characterization

Magnetic levitation (MagLev) provides a simple but promising method for density-based analysis and detection down to the individual cell level. However, each existing MagLev configuration for the single-cell density measurement, mainly consisting of a capillary (∼50 mm) placed between two magnets, yields a fairly low sample utilization because of no knowledge about the sample cells in the regions other than the limited microscope vision. Moreover, the quantitative analysis may be affected due to the unclearly defined measurement area, which is specifically associated with the uneven magnetization of magnets, cell size, degree of aggregation. In this work, we explore a pump-free microfluidic magnetic levitation approach for density-based cell characterization, enabling sensitive and effective cellular density measurement on small sample volumes. The microfluidic MagLev comprises a pump-free microfluidic chip placed between two ring magnets with like poles facing. With no external pumps, connectors or control facility, much smaller amounts of fluids (∼4 μL) could be driven automatically in the entire microchannel in 16 s. Based on the pump-free mechanism, unique density signatures of cells from different lineages (ARPE-19, HCT116, HeLa, HT1080, Huh7) are characterized by monitoring the levitation profiles. Furthermore, variation in density of A549 lung cancer cells subjected to a drug treatment are observed in our platform, allowing evaluation of the efficacy of the drug treatment at the individual cell level. Thereby, the proposed pump-free microfluidic MagLev platform, a low-cost, fully automatic and portable design for label-free density-based cell characterization, provides a universal detection tool that operates efficiently within small-volume environments.

DenVar: density-based variation analysis of multiplex imaging data.

Multiplex imaging platforms have become popular for studying complex single-cell biology in the tumor microenvironment (TME) of cancer subjects. Studying the intensity of the proteins that regulate important cell-functions becomes extremely crucial for subject-specific assessment of risks. The conventional approach requires selection of two thresholds, one to define the cells of the TME as positive or negative for a particular protein, and the other to classify the subjects based on the proportion of the positive cells. We present a threshold-free approach in which distance between a pair of subjects is computed based on the probability density of the protein in their TMEs. The distance matrix can either be used to classify the subjects into meaningful groups or can directly be used in a kernel machine regression framework for testing association with clinical outcomes. The method gets rid of the subjectivity bias of the thresholding-based approach, enabling easier but interpretable analysis. We analyze a lung cancer dataset, finding the difference in the density of protein HLA-DR to be significantly associated with the overall survival and a triple-negative breast cancer dataset, analyzing the effects of multiple proteins on survival and recurrence. The reliability of our method is demonstrated through extensive simulation studies. The associated R package can be found here, https://github.com/sealx017/DenVar. Supplementary data are available at Bioinformatics Advances online.

HologLev: A Hybrid Magnetic Levitation Platform Integrated with Lensless Holographic Microscopy for Density-Based Cell Analysis.

In clinical practice, a variety of diagnostic applications require the identification of target cells. Density has been used as a physical marker to distinguish cell populations since metabolic activities could alter the cell densities. Magnetic levitation offers great promise for separating cells at the single cell level within heterogeneous populations with respect to cell densities. Traditional magnetic levitation platforms need bulky and precise optical microscopes to visualize levitated cells. Moreover, the evaluation process of cell densities is cumbersome, which also requires trained personnel for operation. In this work, we introduce a device (HologLev) as a fusion of the magnetic levitation principle and lensless digital inline holographic microscopy (LDIHM). LDIHM provides ease of use by getting rid of bulky and expensive optics. By placing an imaging sensor just beneath the microcapillary channel without any lenses, recorded holograms are processed for determining cell densities through a fully automated digital image processing scheme. The device costs less than $100 and has a compact design that can fit into a pocket. We perform viability tests on the device by levitating three different cell lines (MDA-MB-231, U937, D1 ORL UVA) and comparing them against their dead correspondents. We also tested the differentiation of mouse osteoblastic (7F2) cells by monitoring characteristic variations in their density. Last, the response of MDA-MB-231 cancer cells to a chemotherapy drug was demonstrated in our platform. HologLev provides cost-effective, label-free, fully automated cell analysis in a compact design that could be highly desirable for laboratory and point-of-care testing applications.

Understanding the Trends in Blockchain Domain Through an Unsupervised Systematic Patent Analysis

Patent analysis is crucial for technology monitoring, forecasting, and assessment, and facilitates entrepreneurs and different stakeholder groups to develop forward-looking technologies and business strategies. However, the speed and scale in the development of disruptive technologies, such as blockchain, present a challenge for analysts and experts. In this article, we propose an unsupervised systematic patent analysis framework that applies a mixture of cosine-based and density-based outlier analysis to the patent space. A sample of 13 393 blockchain-related patents published between January 2014 and June 2020 is used to test the proposed framework. Specifically, this framework merges cosine and density-based outlier detection methodologies to improve the identification of outliers within clusters of patents. The identified outliers are visualized through an age-outlier technology-opportunity analysis map that represents the different levels of novelty existing in each cluster of the patent sample. The map facilitates companies to better target their R&D efforts and maximize the return of technology investments. Benchmark results show that the proposed outlier detection method improves recall, precision, and f1 score. In addition, the results show that the cluster with a higher percentage of outliers represents the Internet of Things applications of blockchain technology.

Portable magnetic levitation technologies

Abstract Magnetic levitation (MagLev) is a density-based method which uses magnets and a paramagnetic medium to suspend multiple objects simultaneously as a result of an equilibrium between gravitational, buoyancy, and magnetic forces acting on the particle. Early MagLev setups were bulky with a need for optical or fluorescence microscopes for imaging, confining portability, and accessibility. Here, we review design criteria and the most recent end-applications of portable smartphone-based and self-contained MagLev setups for density-based sorting and analysis of microparticles. Additionally, we review the most recent end applications of those setups, including disease diagnosis, cell sorting and characterization, protein detection, and point-of-care testing.

The impact of impervious surface, vegetation, and soil areas on land surface temperatures in a semi-arid region using Landsat satellite images enriched with Ndaisi method data.

Impervious surfaces are a significant issue of both urbanization and environmental assessment. However, it is a problem to classify impervious surface (IS) and soil areas as separate classes in land cover classification. The objectives of this study are to obtain impervious surface, vegetation, and soil areas clearly of an urban complex with a semi-arid climate and to better determine the relationships of IS, vegetation, and soil areas with land surface temperatures (LSTs). For this purpose, IS, vegetation, and soil areas in a semi-arid city of Turkey-Kayseri city were identified by using Normalized Difference Anthropogenic Impervious Surface Index (NDAISI) data and support vector machine (SVM) method together in the classification of different areas. Landsat 5, 7, and 8 satellite images of 1987, 2000, and 2013 were used, respectively, in this study. Afterward, the effects of these areas on LSTs were analyzed. Regression analysis was used to determine the relationships between land cover areas and surface temperatures. To better demonstrate these relationships, besides common pixel-based and classical regional-based approaches, a new density-based regional analysis approach was proposed. This study is an innovative one in terms of detecting IS and indicating relationships between land cover areas and surface temperatures in semi-arid regions. Another innovation of the study is related to the results produced. The results showed that decreasing LST values were observed with increasing IS and vegetation cover values and increasing LST values were observed with increasing soil areas. The present findings may provide significant contributions to the literature and will facilitate the development of urban planning strategies in semi-arid regions.

Monitoring the Effective Density of Airborne Nanoparticles in Real Time Using a Microfluidic Nanoparticle Analysis Chip.

Determining the effective density of airborne nanoparticles (NPs; particles smaller than 100 nm in diameter) at a point of interest is essential for toxicology and environmental studies, but it currently requires complex analysis systems comprising several high-precision instruments as well as a specially trained operator. To address these limitations, a field-portable and cost-efficient microfluidic NP analysis device is presented, which provides quantitative information on the effective density and size distribution of NPs in real time. Unlike conventional analysis systems, the device can operate in a standalone mode because of the chip operating principle based on the electrostatic/inertial classification and electrical detection methods. Moreover, the device is both compact (16.0 × 10.9 × 8.6 cm3) and light (950 g) owing to the hardware strip down enabled by integrating the essential functions for effective density analysis on a single chip. Quantitative experiments performed to simulate real-life applications utilizing effective density (i.e., effective density-based morphology analysis on engineered NPs and multi-parametric NP monitoring in ambient air) demonstrate that the developed device can be used as an analysis tool in toxicological studies as an on-site sensor for the monitoring of individual NP exposure and environments, for quality monitoring of engineered NPs via aerosol synthesis, and other applications.