Gaussian Mixture Research Articles

Toward a realistic theoretical electronic spectra of metal aqua ions in solution: The case of Ce(H2O)n3+ using statistical methods and quantum chemistry calculations.

Accurately predicting spectra for heavy elements, often open-shell systems, is a significant challenge typically addressed using a single cluster approach with a fixed coordination number. Developing a realistic model that accounts for temperature effects, variable coordination numbers, and interprets experimental data is even more demanding due to the strong solute-solvent interactions present in solutions of heavy metal cations. This study addresses these challenges by combining multiple methodologies to accurately predict realistic spectra for highly charged metal cations in aqueous media, with a focus on the electronic absorption spectrum of Ce3+ in water. Utilizing highly correlated relativistic quantum mechanical (QM) wavefunctions and structures from molecular dynamics (MD) simulations, we show that the convolution of individual vertical transitions yields excellent agreement with experimental results without the introduction of empirical broadening. Good results are obtained for both the normalized spectrum and that of absolute intensity. The study incorporates a statistical machine learning algorithm, Gaussian Mixture Models-Nuclear Ensemble Approach (GMM-NEA), to convolute individual spectra. The microscopic distribution provided by MD simulations allows us to examine the contributions of the octa- and ennea-hydrate of Ce3+ in water to the final spectrum. In addition, the temperature dependence of the spectrum is theoretically captured by observing the changing population of these hydrate forms with temperature. We also explore an alternative method for obtaining statistically representative structures in a less demanding manner than MD simulations, derived from QM Wigner distributions. The combination of Wigner-sampling and GMM-NEA broadening shows promise for wide application in spectroscopic analysis and predictions, offering a computationally efficient alternative to traditional methods.

Pengelompokan Provinsi di Indonesia Menggunakan Gaussian Mixture Model Berdasarkan Indikator Kemiskinan

Poverty is a multidimensional concept that can not only be seen from an economic perspective, but can also be seen from a social, cultural and political perspective. Poverty is generally expressed as a static concept with the amount of poverty as a description of welfare conditions at a certain time. In reality, poverty has a time period that is never cut off and has a continuous pattern over time. The Indonesian government is committed to efforts to eradicate poverty in order to reduce Indonesia's poverty rate. One way that can be done is by allocating aid to the community. Grouping is carried out by cluster analysis using the Gaussian Mixture Model method with the Expectation-Maximization (EM) algorithm. The optimal number of clusters in the Gaussian Mixture Model uses the Bayesian Information Criterion (BIC) method. Based on the research carried out, the results of the analysis of cluster grouping using the Gaussian Mixture Model method were obtained with the smallest BIC value, namely -397.6876 with a total of 2 clusters.

Revealing disease subtypes and heterogeneity in common variable immunodeficiency through transcriptomic analysis

Common Variable Immunodeficiency (CVID) is a primary immunodeficiency characterized by reduced levels of specific immunoglobulins, resulting in frequent infections, autoimmune disorders, increased cancer risk, and diminished antibody production despite an adequate B cell count. With its clinical manifestations being highly variable, the classification of CVID, including the widely recognized Freiburg classification, is primarily based on clinical symptoms and genetic variations. Our study aims to refine the classification of CVID by analyzing transcriptomics data to identify distinct disease subtypes. We utilized the GSE51405 dataset, examining transcriptomic profiles from 30 CVID patients without complications. Employing a combination of clustering techniques—KMeans, hierarchical agglomerative clustering, spectral clustering, and Gaussian Mixture models—and differential gene expression analysis with R’s limma package, we integrated molecular findings with demographic data (age and gender) through correlation analysis and identified common genes among clusters. Three distinct clusters of CVID patients were identified using KMeans, Agglomerative Clustering, and Gaussian Mixture Models, highlighting the disease’s heterogeneity. Differential expression analysis unveiled 31 genes with variable expression levels across these clusters. Notably, nine genes (EIF5A, RPL21, ANP32A, DTX3L, NCF2, CDC42EP3, CHP1, FOLR3, and DEFA4) exhibited consistent differential expression across all clusters, independent of demographic factors. The study recommends categorizing patients based on the four genes, NCF2, CHP1, FOLR3, and DEFA4—as they may assist in prognostic prediction. Transcriptomic analysis of common variable immunodeficiency (CVID) patients identified three distinct clusters based on gene expression, independent of age and gender. Nine differentially expressed genes were identified across these clusters, suggesting potential biomarkers for CVID subtype classification. These findings highlight the genetic heterogeneity of CVID and provide novel insights into disease classification and potential personalized treatment approaches.

A Robust Trajectory Multi-Bernoulli Filter for Superpositional Sensors

This paper proposes a trajectory multi-Bernoulli filter applied to the superpositional sensor model for multi-target tracking in the presence of unknown measurement noise. This filter can provide a Multi-Bernoulli approximation of the posterior density on a set of alive trajectories at the current time step. We also provide a Gaussian mixture (GM) implementation of this filter, employing a mixture of Gaussian and inverse Wishart distributions to represent the combined state of measurement noise and target information. Subsequently, the variational Bayesian (VB) method is employed to approximate the posterior distribution, ensuring its form remains consistent with the prior distribution. This method is capable of directly generating trajectory estimates and can jointly estimate both multi-object tracking and measurement noise covariance. The performance of this algorithm is verified through simulation. Finally, a computationally more efficient L-scan approximation is provided. The simulation results indicate that the filter can achieve robust tracking performance, adapting to unknown measurement noise.

Efficient detection of data entry errors in large-scale public health surveys: an unsupervised machine learning approach

Data entry errors in large-scale public health surveys can undermine the effectiveness of data-driven interventions. Therefore, identifying these data entry errors is crucial for public health experts. In large-scale public health surveys, manually verifying the accuracy of every data point by domain experts is nearly impossible. This study evaluates unsupervised machine learning algorithms for detecting these errors, focusing on the 'weight' parameter in the Annual Health Survey (AHS) dataset. The AHS, conducted by the Ministry of Health and Family Welfare, Government of India, in collaboration with the Registrar General of India, is a large-scale, stratified, household-level survey targeting maternal and child health across nine states in India. The dataset is freely available on the Open Government Data (OGD) Platform of India for public health research. In this study, five algorithms—DBSCAN, K-Means, Gaussian Mixture Model (GMM), Isolation Forest (IF), and One-Class SVM (1C-SVM) were applied to detect erroneous data entries. The evaluation process involved comprehensive preprocessing and feature engineering to optimize detection capabilities. Performance metrics such as precision, recall, accuracy, false anomaly, and missed anomaly rates were used to assess each algorithm. Among these, DBSCAN demonstrated superior performance, achieving a recall of 94.7% and a precision of 81.9%, making it highly effective for this task. The findings underscore the potential of unsupervised machine learning in automating the detection of data entry errors, thereby improving the integrity of public health data. This research contributes to the advancement of precision public health, supporting more accurate and reliable evidence-based decision-making and policy formulation.

Experience-based noise model of infrared detectors for system dimensioning and simulation

We show that infrared detectors frequently exhibit non-Gaussian spatial noise, which makes it difficult to compare their performance. The power-law-like behavior we show can be very detrimental to certain detection missions, hence the need to characterize this noise. We demonstrate that a simple mixture of Gaussian and Student processes corresponds to the observed example, and that the parameter determination procedure described here reaches its theoretical limit.

Gaussian Mixture Regression Model with Sparsity for Clustering of Territory Risk in Auto Insurance

Abstract Insurance rating territory design and accurate estimation of territory risk relativities are fundamental aspects of auto insurance rate regulation. It is crucial to develop methodologies that can facilitate the effective design of rating territories and their risk relativities estimate, as they directly impact the rate filing and the decision support of the rate change review process. This article proposes a Gaussian Mixture Regression model clustering approach for territory design. The proposed method incorporates a linear regression model, taking spatial location as model covariates, which helps estimate the cluster mean more accurately. Also, to further enhance the estimation of territory risk relativities, we impose sparsity through sparse matrix decomposition of the membership coefficient matrix obtained from the Gaussian Mixture Regression model. By transitioning from the current hard clustering method to a soft approach, our methodology could improve the evaluation of territory risk for rate-making purposes. Moreover, using non-negative sparse matrix approximation ensures that the estimation of risk relativities for basic rating units remains smooth, effectively eliminating data noise from the territory risk relativity estimate. Overall, our novel methodology aims to significantly enhance the accuracy and reliability of risk analysis in auto insurance. Furthermore, the proposed method exhibits potential for extension to various other domains that involve spatial clustering of data, thereby broadening its applicability and expanding its usefulness beyond auto insurance rate regulation.

Uncertainty prediction of conventional gas production in Sichuan Basin under multi factor control

The establishment of a natural gas production model under multi factor control provides support for the formulation of planning schemes and exploration deployment decisions, and is of great significance for the rapid development of natural gas. Especially the growth rate and decline rate of production can be regulated in the planning process to increase natural gas production. The exploration and development of conventional gas in the Sichuan Basin has a long history. Firstly, based on the development of conventional gas production, the influencing factors of production are determined and a production model under multi factor control is established. Then, single factor analysis and sensitivity analysis are conducted, and multi factor analysis is conducted based on Bayesian networks. Finally, combining the multivariate Gaussian mixture model and production sensitivity analysis, a production planning model is established to predict production uncertainty under the influence of multiple factors. The results show that: 1) the production is positively correlated with the five influencing factors, and the degree of influence is in descending order: recovery rate, proven rate, growth rate, decline rate, and recovery degree. After being influenced by multiple factors, the fluctuation range of production increases and the probability of realization decreases. 2) The growth rate controls the amplitude of the growth stage, the exploration rate and recovery rate control the amplitude of the stable production stage, the recovery degree controls the amplitude of the transition from the stable production stage to the decreasing stage, and the decreasing rate controls the amplitude of the decreasing stage. 3)The article innovatively combines multiple research methods to further obtain the probability of achieving production under the influence of multiple factors, providing a reference for the formulation of production planning goals.

Data-driven Approximation of Cumulative Distribution Function Using Particle Swarm Optimization based Finite Mixtures of Logistic Distribution

This paper proposes a data-driven approximation of the Cumulative Distribution Function using the Finite Mixtures of the Cumulative Distribution Function of Logistic distribution. Since it is not possible to solve the logistic mixture model using the Maximum likelihood method, the mixture model is modeled to approximate the empirical cumulative distribution function using the computational intelligence algorithms. The Probability Density Function is obtained by differentiating the estimate of the Cumulative Distribution Function. The proposed technique estimates the Cumulative Distribution Function of different benchmark distributions. Also, the performance of the proposed technique is compared with the state-of-the-art kernel density estimator and the Gaussian Mixture Model. Experimental results on κ−μ distribution show that the proposed technique performs equally well in estimating the probability density function. In contrast, the proposed technique outperforms in estimating the cumulative distribution function. Also, it is evident from the experimental results that the proposed technique outperforms the state-of-the-art Gaussian Mixture model and kernel density estimation techniques with less training data.

Multi‐colour recipe prediction through invertible neural network and clustering algorithm

AbstractComputer‐assisted colour prediction has become increasingly significant for various consumer items in the industrial sector. Many professional colourists find it challenging to develop a suitable colour recipe. The most difficult aspect is predicting which dye mix will result in the required shade on a given fabric. This study introduces an advanced alternative to the traditional colour‐making method using an invertible neural network (INN) model. The INN model effectively addresses real‐world inverse problems due to its bi‐directional nature. In the forward phase, the model retrieves information about the colour recipe; in the backward phase, this information is combined with latent space data to predict the recipe. Furthermore, unsupervised data generated by the INN model is fed into clustering algorithms, such as K‐means and the Gaussian mixture model (GMM), to obtain multiple recipes. The forward procedure was reintroduced with a predicted recipe to assess the efficacy of the proposed model. An analysis was then conducted on the colour differences between the anticipated and actual recipes. The colour differences, rounded to perceptually significant precision, from 30,000 samples with 50 centre points, are as follows: 1.4, 2.2, 2.5, 3.7, 1.2, and 2.1. These results indicate that the INN model and the GMM clustering approach together provide a highly accurate and efficient solution to automating the colour‐matching process, offering a more precise and practical solution for the colour‐manufacturing industry.

An unsupervised approach for the detection of zero‐day distributed denial of service attacks in Internet of Things networks

AbstractThe authors introduce an unsupervised Intrusion Detection System designed to detect zero‐day distributed denial of service (DDoS) attacks in Internet of Things (IoT) networks. This system can identify anomalies without needing prior knowledge or training on attack information. Zero‐day attacks exploit previously unknown vulnerabilities, making them hard to detect with traditional deep learning and machine learning systems that require pre‐labelled data. Labelling data is also a time‐consuming task for security experts. Therefore, unsupervised methods are necessary to detect these new threats. The authors focus on DDoS attacks, which have recently caused significant financial and service disruptions for many organisations. As IoT networks grow, these attacks become more sophisticated and harmful. The proposed approach detects zero‐day DDoS attacks by using random projection to reduce data dimensionality and an ensemble model combining K‐means, Gaussian mixture model, and one‐class SVM with a hard voting technique for classification. The method was evaluated using the CIC‐DDoS2019 dataset and achieved an accuracy of 94.55%, outperforming other state‐of‐the‐art unsupervised learning methods.

Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data

Flood mapping using Synthetic Aperture Radar (SAR) data impose limitations in fully distinguishing flood under vegetation due to false double bounce returns from inundated tree trunks along with seasonal heterogeneities devised from changing land cover settings. In addition, rapid mapping of flooded vegetation is challenging during near real time applications. In this paper a fully automatic novel supervised classification approach called polarimetric Naïve Bayes is proposed that combines polarimetric information with series of Gaussian mixture models in Naïve Bayes framework to detect various flooded vegetation classes. It also allows the user to choose class configuration and eliminates creation of Region of Interest (ROI) for supervised training. The proposed approach uses scattering information from pre monsoon PolSAR dataset in training step to create ROIs for buildings and other features. In the next step series of Gaussian Mixtures are used for density estimation for different features in Bayesian multiclass problem. The newly developed classifier applied on 2016 Assam flood event resulted in precise mapping of at least three different vegetation classes under flood such as submerged vegetation, wetlands and floating vegetation. Under optimal class configuration, the approach showed better performance compared to other supervised techniques applied on the same data set such as MLE, Mahalanobis, Minimum Euclidean distance, and SVM classifications in delineating flood, submerged vegetation, wetlands and floating vegetation with Producer’s Accuracy of 98.6%, 81.1%, 94% and 51.5% respectively and combined Overall accuracy of 95.5% for flooded vegetation class. This method also detected multiple vegetation classes with better accuracy compared to similar methods.

6818 Adherence Risk Management in Patients on Recombinant Human Growth Hormone Therapy, With Traffic Light Alerting Systems Tailored for Both Weekday and Weekend Adherence

Abstract Disclosure: S. Loche: Advisory Board Member; Self; SL has received advisory board fees from Merck KGaA, Darmstadt, Germany. Consulting Fee; Self; SL has received consultancy fees from Merck KGaA, Darmstadt, Germany. Speaker; Self; SL has received lecture fees from Merck KGaA, Darmstadt, Germany. P. van Dommelen: Consulting Fee; Self; PvD has a consultancy agreement with Merck KGaA, Darmstadt, Germany. V. Tornincasa: Employee; Self; VT is an employee of Ares Trading SA, an affiliate of Merck KGaA. L. Arnaud: Employee; Self; LA is an employee of Ares Trading SA, an affiliate of Merck KGaA. E. Koledova: Employee; Self; EK is an employee of Merck KGaA,Darmstadt, Germany. Stock Owner; Self; EK is an employee of Merck KGaA,Darmstadt, Germany. Background: The integration of data-driven strategies, such as clustering analysis with an alerting system, empowers healthcare professionals with actionable insights. In this study, we focused on clustering adherence patterns during the weekdays and weekends in the first 12 weeks of treatment. Our aim is to develop a data-driven clinical decision support system based on "traffic light" visualizations addressing adherence challenges during both weekdays and weekends in patients receiving Recombinant Human Growth Hormone Therapy. Data and Methods: Adherence data between March-November 2023 were extracted from the newly launched third generation of easypod™ device (EP3) and GrowZen Connect Next ecosystem. EP3 is the only connected device that delivers r-hGH and monitors real-time adherence to therapy. EP3 was perceived by health care professionals as more intuitive, comfortable, user-friendly, simpler, and easier to use than previous EP2. Patients with age 2-18 years at treatment start, a 7-day regimen and complete adherence data available between 1-12 weeks of treatment were selected. Patients’ adherence to therapy was represented using mean and standard deviation (SD) of adherence (%) and the interquartile range (IQR) of the timing of injections throughout the day in minutes aggregated by weekday (Monday-Thursday) and weekend day (Friday-Sunday). Cluster analysis was used to categorize adherence patterns using a Gaussian mixture model. Following a traffic lights-inspired visualization approach, the algorithm was set to create three clusters: high (green), medium (yellow), and low adherence (red). The area under the receiver operating characteristic curve (AUC-ROC) was used to find optimum thresholds for independent traffic lights. Results: Data for 232 patients (124 boys and 108 girls, median (Q25-Q75) age at treatment start was 10.5 (7.6-12.3)) were available. The most appropriate traffic light used the SD of adherence during the weekend, with an AUC-ROC value of 0.87. For the weekday, adherence-based traffic lights using optimum thresholds were >98% (SD<10.2), 88-98% (SD:10.2-22.8) and <88% (SD>22.8) for high, medium, and low adherence, respectively. For IQR of the timing of injections throughout the weekday, optimum thresholds were <38, 38-70 and >70 (min). For the weekend, optimum thresholds were >96% (SD<7.7), 86-96% (SD:7.7-21.5) and <86% (SD>21.5). For IQR of the timing of injections throughout the weekend, optimum thresholds were <56, 56-84 and >84 (min). Conclusions: Our research indicates that implementing a practical data-driven alert system, utilizing our proposed traffic-light coding, would empower healthcare practitioners to monitor patients' adherence to growth hormone therapy comprehensively. By understanding patient behavior during the weekdays and weekend, healthcare practitioners can tailor interventions for improved treatment outcomes. Presentation: 6/3/2024

Analysis of Complexome Profiles with the Gaussian Interaction Profiler (GIP) Reveals Novel Protein Complexes in Plasmodium falciparum.

Complexome profiling is an experimental approach to identify interactions by integrating native separation of protein complexes and quantitative mass spectrometry. In a typical complexome profile, thousands of proteins are detected across typically ≤100 fractions. This relatively low resolution leads to similar abundance profiles between proteins that are not necessarily interaction partners. To address this challenge, we introduce the Gaussian Interaction Profiler (GIP), a Gaussian mixture modeling-based clustering workflow that assigns protein clusters by modeling the migration profile of each cluster. Uniquely, the GIP offers a way to prioritize actual interactors over spuriously comigrating proteins. Using previously analyzed human fibroblast complexome profiles, we show good performance of the GIP compared to other state-of-the-art tools. We further demonstrate GIP utility by applying it to complexome profiles from the transmissible lifecycle stage of malaria parasites. We unveil promising novel associations for future experimental verification, including an interaction between the vaccine target Pfs47 and the hypothetical protein PF3D7_0417000. Taken together, the GIP provides methodological advances that facilitate more accurate and automated detection of protein complexes, setting the stage for more varied and nuanced analyses in the field of complexome profiling. The complexome profiling data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD050751.

Noise within hospital wards: a survey method

Noise pollution is one of the most disturbing environmental factors. It directly affects people's lives, especially vulnerable people such as children, old-age, and hospital patients. Hospitals are complex buildings with manifold spaces and facilities where different users live with different sensitivity to noise. Several studies highlight the discomfort that patients, staff, and visitors experience in hospitals due to inadequate noise levels. All this leads to stress, sleep disorders, interference in communication, aggression, and annoyance. Noise exposure in hospitals is due to several sound sources. Each varies throughout the day, and their detection could be challenging. The present study proposes a clustering technique to analyze long-term sound level meter monitoring in an Italian hospital (Pisa, Tuscany). Equivalent short-term levels obtained with an interval time of 0.1 s are used as the database to exploit the occurrences curves of different chunks of the monitoring. Then, through a validated method, the spectra of different kinds of sound sources are reconstructed via the Gaussian Mixture Model. Results are compared with near-field measurements of single sources.

The role of person-related factors and acoustic parameters on students' perception: a pilot study

Classrooms are dynamic spaces where the sound environment not only influences the comprehension of the teacher's message but also shapes the social and emotional atmosphere within the class. This pilot study investigates the sound perception of students within classrooms, considering both person-related and acoustic objective factors as predictors. Thirty-four students from two grade-four classes of a primary school in Pavia, Italy, participated in this study, completing a questionnaire assessing the perceived pleasantness, arousal and loudness within the classroom. Person-related factors such as noise sensitivity, working memory, theory of mind, and language skills were also evaluated separately. Sound levels in occupied conditions were monitored and processed using Gaussian Mixture Models to separate teacher speech levels from students' activity. Additionally, acoustic parameters including reverberation time and clarity were measured in unoccupied conditions. Person-related factors were analysed in relation to subjective evaluations, considering the potential mediating effect of the classroom sound environment. The results of this study will offer insights into the complex interplay between person-related characteristics and the sound environment, shedding light on factors beyond noise interferences that impact students' subjective perception in educational settings.

ZTF SN Ia DR2: Impact of the galaxy cluster environment on the stretch distribution of Type Ia supernovae

Understanding the impact of the astrophysical environment on Type Ia supernova (SN Ia) properties is crucial to minimize systematic uncertainties in cosmological analyses based on this probe. We investigate the dependence of the SN Ia SALT2.4 light-curve stretch on the distance from their nearest galaxy cluster to study the potential effect of the intracluster medium (ICM) environment on the intrinsic properties of SN Ia. We used the largest SN Ia sample to date and cross-matched it with existing X-ray, Sunyaev-Zel'dovich, and optical cluster catalogs in order to study the relation between the stretch and the distance to the nearest detected cluster from each SN Ia. We modeled the underlying stretch distribution with a Gaussian mixture with relative amplitudes that depended on redshift and clustercentric distance. We find that the fit quality of the stretch distribution improves significantly when we included the distance-dependant term in the model with a variation of the Akaike information criterion $ AIC = -10.2$. Because of the known correlation between galaxy age and distance from the cluster center, this supports previous evidence that the age of the stellar population is the underlying driver of the bimodal shape of the SN Ia stretch distribution. We further computed the evolution of the fraction of quenched galaxies as a function of distance with respect to cluster center from our best-fit model of the SNe Ia stretch distribution and compared it to previous results obtained from $H line measurements, optical broadband photometry, and simulations. We find our estimate to be compatible with these results. The results of this work indicate that SNe Ia searches at high redshift targeted toward clusters to maximize detection probability should be considered with caution as the stretch distribution of the detected sample would be strongly biased toward the old subpopulation of SNe Ia. Furthermore, the effect of the ICM environment on the SN Ia properties appears to be significant from the center of the clusters up to their splashback radius. This is compatible with previous works based on observations and simulations of a galaxy age gradient with respect to clustercentric distance in massive cluster halos. The next generation of large-area surveys will provide an increase of an order of magnitude in the size of SN Ia and cluster catalogs. This will enable us to analyze the impact of cluster mass on the intrinsic properties of SNe Ia and of the fraction of quenched galaxies in the outskirts of clusters in more detail, where direct measurements are challenging.

Efficient Modeling of Spatial Extremes over Large Geographical Domains

Various natural phenomena exhibit spatial extremal dependence at short spatial distances. However, existing models proposed in the spatial extremes literature often assume that extremal dependence persists across the entire domain. This is a strong limitation when modeling extremes over large geographical domains, and yet it has been mostly overlooked in the literature. We here develop a more realistic Bayesian framework based on a novel Gaussian scale mixture model, with the Gaussian process component defined by a stochastic partial differential equation yielding a sparse precision matrix, and the random scale component modeled as a low-rank Pareto-tailed or Weibull-tailed spatial process determined by compactly-supported basis functions. We show that our proposed model is approximately tail-stationary and that it can capture a wide range of extremal dependence structures. Its inherently sparse structure allows fast Bayesian computations in high spatial dimensions based on a customized Markov chain Monte Carlo algorithm prioritizing calibration in the tail. We fit our model to analyze heavy monsoon rainfall data in Bangladesh. Our study shows that our model outperforms natural competitors and that it fits precipitation extremes well. We finally use the fitted model to draw inference on long-term return levels for marginal precipitation and spatial aggregates.

A robust detection scheme against selective forwarding attacks in wireless sensor networks under variable harsh environments

Event-driven wireless sensor networks (EWSNs) often operate in harsh environments and are susceptible to selective forwarding attacks, where malicious nodes selectively drop packets. These attacks are difficult to detect due to similarities with normal node behavior under adverse conditions. To address this, we propose a detection scheme combining Variable Autoencoder (VAE) and Gaussian Mixture Model (GMM) with an Autoencoder (AE). The scheme extracts features from the node's single round forwarding rate (SFR), with VAE generating latent variables and GMM clustering them to differentiate behaviors. The AE model, trained on data representing normal behavior, uses reconstruction error to identify malicious nodes. Simulation results show that our scheme improves network throughput and achieves low False Detection Rate (FDR) and Missed Detection Rate (MDR), with both rates as low as 0.5% in normal conditions and below 2% in mobile harsh environments. With a computational complexity of O(n), this method is suitable for resource-constrained environments, demonstrating superior robustness compared to existing approaches.

A-167 Hematologic setpoints are a deep physiologic phenotype which enable personalized blood count reference intervals

Abstract Background The complete blood count (CBC) is an important screening tool for healthy adults, commonly ordered at periodic physical exams. However, test results are typically interpreted using one-size-fits-all reference intervals (RIs), which don’t account for the low index-of-individuality of most CBC markers. This undermines the goal of precision medicine - to tailor patient care based on individual characteristics. Methods Here we utilize a database of >50,000 healthy outpatients with yearly CBCs, to investigate the value of patient-specific reference intervals (PRIs) for 9 CBC markers. We use Gaussian mixture modelling to calculate each patient’s setpoints, and to construct associated PRIs. Mechanistic drivers of setpoints were investigated through GWAS and via analysis of excess clinical specimens. Clinical outcomes were analyzed by comparing sensitivity of RIs and PRIs for detection of future cytopenia/cythemia and mortality. Results We found healthy adults had patient-specific setpoints that were stable over decades, and distinctly identifiable from 98% of other patients. Setpoints reflected a deep physiologic phenotype, enabling improved detection of genetic determinants of hematologic regulation (Fig1A), including discovery of multiple novel loci via GWAS analysis. Deviation from PRIs was associated with greater risk of cytopenia/cythemia and mortality (Fig1B) than deviation from the RI for all setpoints except MCHC. Setpoints were significantly correlated with long-term all-cause mortality, even when limited to those within the RI (Fig1C). Conclusions We show that hematologic setpoints reflect a deep, patient-specific phenotype. This phenotype can enhance mechanistic studies, and allow for increased diagnostic sensitivity, through calculation of a personalized reference interval. Setpoints are sufficiently stable and patient-specific to help realize the promise of precision medicine for healthy adults.Fig1A.Significance of GWAS hits using setpoints versus single CBCs. Fig1B. 10yr mortality hazard ratio for tests outside the RI and PRI. Fig1C. Association of WBC setpoint with mortality (limited to WBC setpoints inside the RI).