Unsupervised Machine Learning Methods Research Articles

Self-Supervised Machine Learning Framework for Online Container Security Attack Detection

Container security has received much research attention recently. Previous work has proposed to apply various machine learning techniques to detect security attacks in containerized applications. On one hand, supervised machine learning schemes require sufficient labeled training data to achieve good attack detection accuracy. On the other hand, unsupervised machine learning methods are more practical by avoiding training data labeling requirements, but they often suffer from high false alarm rates. In this paper, we present a generic self-supervised hybrid learning (SHIL) framework for achieving efficient online security attack detection in containerized systems. SHIL can effectively combine both unsupervised and supervised learning algorithms but does not require any manual data labeling. We have implemented a prototype of SHIL and conducted experiments over 46 real world security attacks in 29 commonly used server applications. Our experimental results show that SHIL can reduce false alarms by 33-93% compared to existing supervised, unsupervised, or semi-supervised machine learning schemes while achieving a higher or similar detection rate.

Unsupervised machine learning defines subgroups in ischemic heart disease patients presenting with ventricular tachycardia

Abstract Background Ventricular tachycardia (VT) in ischemic heart disease (IHD) is the leading cause of sudden cardiac death (SCD). Despite the well-defined recommendations, tailoring therapies individually is often a challenge owing to the great heterogeneity in this population. We hypothesized that unsupervised machine learning can uncover hidden patterns to identify patient subgroups with similar characteristics. Aims To use unsupervised machine learning methods on a database of patients with ischemic VT, to discover subgroups with similar characteristics and compare the survival of the identified groups. Methods We collected the data of 566 patients with IHD hospitalized for sustained monomorphic VT, excluding those with acute coronary syndrome. The 17 recorded features included medical history and echocardiography. All-cause mortality was registered with one-year follow-up. We performed dimension reduction using T-distributed Stochastic Neighbor Embedding (TSNE) to produce 3 dimensions of the data. Then, we applied spectral clustering to create three groups within the population. Each group was compared to the rest of the population for each variable. Finally, we compared the survival across the three groups using Kaplan-Meyer analysis, univariate Cox-regression, and Log-rank test. Results We identified three groups with distinct characteristics. Group 1 represented the healthiest population; patients had no previous VT hospitalizations, and none of HD instability, LBBB, SCD, diabetes, VT storm or ICD shocks; additionally, they always had a single VT episode at presentation. They showed better left ventricular ejection fraction (LVEF) (38% vs. 33%, p<0.001) and TAPSE (20 mm vs. 18 mm, p=0.01), less severe mitral and tricuspid regurgitation (MR, TR), smaller left ventricular end-systolic diameter, and longer DT. Conversely, patients in Group 2 (n=349) had more previous VT hospitalizations (30% vs. 18%, p=0,003), more often had diabetes (46% vs. 18%, p<0.001), left bundle branch block (23% vs. 13%, p=0.006), and more often presented with HD instability (47% vs. 28%, p<0.001). Group 3 contained all seriously ill patients with VT storm or incessant VT, more commonly presenting with ICD shocks (34% vs. 21%, p=0.004), SCD (24% vs. 15%, p=0.004), hemodynamic instability (50% vs. 37%, p=0.01). This group had the poorest LVEF (30% vs. 35%, <0.001) and TAPSE (17 mm vs. 19 mm p=0.001), more severe MR and TR, and the shortest DT (140 ms vs. 173 ms p<0.001). Finally, we compared survival across the three groups. Significantly better survival was found in Group 1 (HR: 0.52 CI [0.28 - 0.98]) and Group 2 (HR: 0.61 CI [0.42 - 0.88]), while Group 3 had higher mortality (HR: 2.7 CI [1.83 - 3.97]). Conclusions Using spectral clustering we identified three groups in our cohort of ischemic VT patients. This method delineates the group of ischemic VT patients with the highest risk of mortality who might need more aggressive treatment and closer follow-up.

MousiPLIER: A Mouse Pathway-Level Information Extractor Model.

High throughput gene expression profiling measures individual gene expression across conditions. However, genes are regulated in complex networks, not as individual entities, limiting the interpretability of gene expression data. Machine learning models that incorporate prior biological knowledge are a powerful tool to extract meaningful biology from gene expression data. Pathway-level information extractor (PLIER) is an unsupervised machine learning method that defines biological pathways by leveraging the vast amount of published transcriptomic data. PLIER converts gene expression data into known pathway gene sets, termed latent variables (LVs), to substantially reduce data dimensionality and improve interpretability. In the current study, we trained the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. We then validated the mousiPLIER approach in a study of microglia and astrocyte gene expression across mouse brain aging. mousiPLIER identified biological pathways that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. To gain further insight into the genes contained in LV41, we performed k-means clustering on the training data to identify studies that respond strongly to LV41. We found that the variable was relevant to striatum and aging across the scientific literature. Finally, we built a web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together this study defines mousiPLIER as a method to uncover meaningful biological processes in mouse brain transcriptomic studies.Significance statement RNA-sequencing studies define differential expression of individual genes across conditions. However, genes are regulated in complex networks, not as individual entities. Machine learning models that incorporate biological pathway information are a powerful tool to analyze human gene expression. However, such models are lacking for mouse, despite the vast number of mouse RNA-seq datasets. We trained a mouse pathway-level information extractor model (mousiPLIER) to reduce data dimensionality from over 10,000 genes to 196 'latent variables' that map to known biological pathways. To validate this approach, we applied mousiPLIER to differential expression across mouse brain aging. We identified 26 functional pathways (latent variables) that varied across aging. Finally, we developed a web server to facilitate use of mousiPLIER by the scientific community.

Classification of Users of a Health Service Provider Using Unsupervised Machine Learning Methods

Classification of Users of a Health Service Provider Using Unsupervised Machine Learning Methods

Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables.

The joint analysis of two datasets [Formula: see text] and [Formula: see text] that describe the same phenomena (e.g. the cellular state), but measure disjoint sets of variables (e.g. mRNA vs. protein levels) is currently challenging. Traditional methods typically analyze single interaction patterns such as variance or covariance. However, problem-tailored external knowledge may contain multiple different information about the interaction between the measured variables. We introduce MIASA, a holistic framework for the joint analysis of multiple different variables. It consists of assembling multiple different information such as similarity vs. association, expressed in terms of interaction-scores or distances, for subsequent clustering/classification. In addition, our framework includes a novel qualitative Euclidean embedding method (qEE-Transition) which enables using Euclidean-distance/vector-based clustering/classification methods on datasets that have a non-Euclidean-based interaction structure. As an alternative to conventional optimization-based multidimensional scaling methods which are prone to uncertainties, our qEE-Transition generates a new vector representation for each element of the dataset union [Formula: see text] in a common Euclidean space while strictly preserving the original ordering of the assembled interaction-distances. To demonstrate our work, we applied the framework to three types of simulated datasets: samples from families of distributions, samples from correlated random variables, and time-courses of statistical moments for three different types of stochastic two-gene interaction models. We then compared different clustering methods with vs. without the qEE-Transition. For all examples, we found that the qEE-Transition followed by Ward clustering had superior performance compared to non-agglomerative clustering methods but had a varied performance against ultrametric-based agglomerative methods. We also tested the qEE-Transition followed by supervised and unsupervised machine learning methods and found promising results, however, more work is needed for optimal parametrization of these methods. As a future perspective, our framework points to the importance of more developments and validation of distance-distribution models aiming to capture multiple-complex interactions between different variables.

Soil classification based on unsupervised learning using cone penetration test data

Borehole data obtained during geological surveys are the most essential source for understanding soil stratification. It is a prerequisite to know the soil classes up to some depths prior to any construction. However, the direct method to identify the soil classes by drilling boreholes and testing soil samples is costly. A cost-effective alternative is the Cone Penetration Testing (CPT), which is one of the most popular soil investigation methods. This paper explores the intelligent classification of soil layers based on CPT data using three unsupervised machine learning methods: K-means, Gaussian Mixture Process, and BIRCH. The research investigates the classification performance of different models in scenarios with 2 combinations, 3 combinations, 4 combinations, and 5 combinations. The results indicate that the Gaussian Mixture Process method exhibits the best classification performance, followed by the BIRCH method, while K-means performs relatively poorly. Using unsupervised learning for intelligent soil layer classification offers a fast and clear process, but the accuracy still requires further improvement. This study provides a valuable reference for future soil classification studies.

The performance of unsupervised machine learning method in the indirect reference interval estimation of selected biochemical analytes

The performance of unsupervised machine learning method in the indirect reference interval estimation of selected biochemical analytes

Unsupervised Machine Learning Revealed that Repeat Transcranial Magnetic Stimulation is More Suitable for Stroke Patients with Statin.

Repeat transcranial magnetic stimulation (rTMS) demonstrates beneficial effects for stroke patients, though its efficacy varies due to the complexity of patient conditions and disease progression. Unsupervised machine learning could be the optimal solution for identifying target patients for transcranial magnetic stimulation treatment. We collected data from ischaemic stroke patients treated with rTMS. Unsupervised machine learning methods, including K-means and Hierarchical Clustering, were used to explore the clinical characteristics of patients suitable for rTMS. We then utilized a prospective observational cohort to validate the effect of selected characteristics. For the validated cohort, outcomes included the presence of motor evoked potentials (MEP), favorable functional outcomes (FFO), and changes in the Fugl-Meyer Assessment (FMA) at 3 and 6months. Hierarchical clustering methods revealed that patients in the better prognosis group were more likely to take statins. The validated cohort was grouped based on statin intake. Patients taking statins exhibited a higher rate of MEP (p = 0.006), a higher rate of FFO at 3months (p = 0.003) and 6months (p = 0.021), and a more significant change in FMA (p < 0.001) at both 3 and 6months. Statin intake was associated with FFO and changes in FMA at 3 and 6months. This relationship persisted across all subgroups for FMA changes and some FFO subgroups. Stroke patients undergoing rTMS treatment taking statins exhibited greater MEP, FFO, and changes in FMA. Statin intake was associated with a better prognosis in these patients.

Использование машинного обучения для тематической разметки текстовых материалов корпуса устной речи

The research aims to determine the effectiveness of the thesaurus method for forming a list of topic classes when using machine learning for the topic classification of text materials of sociolinguistic interviews. The paper considers the potential of using machine learning in the topic annotation of linguistic corpus materials. The polytopical nature of the analyzed material is due to its genre belonging to dialogical speech. The hierarchical structure of the topics, identified as a result of a preliminary introspective analysis of the texts, can be described using a thesaurus. The results of using the unsupervised machine learning method are discussed involving two sets of topic class names: a list of topics used in manual text annotation and an extended list of micro-topics whose names were selected from a Russian language thesaurus. The paper is novel in that it is the first to propose the thesaurus method for selecting topic labels for the zero-shot classification of weakly structured Russian texts. The research findings show that using a more detailed lexical description for topic classes improves the classification result.

The application of statistical and novel unsupervised machine learning methodology to forensic hair analysis

ABSTRACT Hair colour is a valuable feature in forensic hair analysis and hair comparisons. Five hundred microscopic images of hair shafts were taken and for each image and colour model (RGB, CIE XYZ and CIE L*a*b*) values for each image were determined. The discriminating power of the three colour model values using statistical and unsupervised machine learning methods was evaluated. Additionally, the discriminating power of the images of the same hair shafts using unsupervised machine learning methods was evaluated. All methods were compared to determine which method had the greatest discriminating power to best distinguished between participants and accurately assigned hair to an individual and assist in forensic hair comparisons. The RGB colour model demonstrated the highest discriminating power of the colour model values while the CIE L*a*b* model achieved complete discrimination with a reduced number of participants. The unsupervised k-means model yielded similar results. Unsupervised PCA/k-means and agglomerative clustering models demonstrated low discrimination power of the images, suggesting the existence of additional features within the data beyond colour. This research highlights the significance of the incorporation of colour values in forensic hair comparisons and for further exploration of the incorporation of other hair features, beyond colour values.

Developing urban building energy models for shanghai city with multi-source open data

Urban building energy modeling is crucial for guiding carbon reduction policies, but acquiring reliable data at the urban scale remains challenging. This study develops a model for Shanghai City, China, by integrating multi-source open data. Eight data sources were collected, including maps, satellite imagery, and GIS data, covering 609,763 building footprints and 539,119 buildings (1.57 billion m2). Spatial analysis, supervised learning, and unsupervised machine learning methods were used to categorize buildings into 63 prototypes, and classification accuracy reached 95 %. Historical satellite data and community boundaries determined the year built for over 95 % of buildings. Prototypes were modeled in AutoBPS using local energy saving standards and simulated in EnergyPlus to derive energy use intensities aligning with government ranges. This work demonstrates a practical data fusion approach to develop large-scale, reliable urban building energy models. Integrating heterogeneous open data sources expands the coverage of open data and improves accuracy. The framework and insights provide a valuable foundation to leverage open data for advancing city-scale energy modeling and sustainability planning.

Research on Soil Pesticide Residue Detection Using an Electronic Nose Based on Hybrid Models

At present, the electronic nose has became a new technology for the rapid detection of pesticides. However, the technique may misidentify them for samples that have not been involved in training. Therefore, a hybrid model based on unsupervised and supervised learning was proposed for the first time in this paper. The model divided the detection process of soil pesticide residues into two steps: (1) an unsupervised machine learning method was used to identify whether the soil was contaminated with pesticides; (2) when the soil was contaminated with pesticides, a supervised classifier was further used to predict the types of pesticides in the soil. The experimental results showed that the model had a recognition accuracy of 99.3% and 99.27% for whether the soil was contaminated with pesticides and the pesticide type of the contaminated soil, respectively, with a detection time of 0.03 s. The results revealed that the proposed hybrid model can quickly and comprehensively reflect the soil information’s status.

Abstract 868: A novel unsupervised machine learning model applied to neuroblastoma single-cell RNA-seq data reveals a drug-induced mesenchymal-like gene expression program

Abstract High-risk neuroblastoma is one of the most difficult to treat pediatric cancers, with a survival rate of only about 50% and significant long-term consequences from current chemotherapy. Preclinical models such as cell lines and mice are the backbone of drug development and experimental-mechanistic oncology, but the development of new treatments is hampered in part by a lack of understanding of the direct clinical relevance of such data collected in preclinical models. Despite this, few formal methods have been developed to determine how these various models represent/resemble primary patient tumors. Here, we present the first comprehensive single-cell RNA-seq analysis of neuroblastoma across an extensive cohort of patient tumors and a variety of preclinical model systems (n = 126 total samples assembled - the largest cohort of its kind). By building an innovative unsupervised machine learning method, which we term “automatic consensus nonnegative matrix factorization” (acNMF), we have integrated and contrasted the transcriptional landscapes of patient tumors with those of cell lines, patient-derived xenografts (PDX), and genetic mouse models (GEMM).Using these tools, we discovered the dominant adrenergic gene expression programs found in neuroblastoma patient tumors were preserved across all preclinical models. However, the presumptive chemo-resistant mesenchymal-like programs, while identifiable in cell lines, were primarily restricted to subpopulations of cancer-associated fibroblasts and Schwann-like cells in vivo. Surprisingly, a mesenchymal-like program could be acutely chemotherapy-induced in GEMM and was evident in pre-treated patient and PDX samples, suggesting a previously uncharacterized mechanism of therapy escape. In addition to these core findings, our computational tools were able to further delineate the classical neuroblastoma adrenergic and mesenchymal gene expression programs, discovering for example, novel subpopulations of cancer associated fibroblasts. These behaviors were conserved across tumors and most preclinical models, which we validated by RNA in situ hybridization, which is a high resolution ultra sensitive spatial transcriptomics technology. Our work cautions against overreliance on traditional preclinical models without recognizing their limitations and we offer a nuanced, high-resolution view of neuroblastoma pre-clinical systems for advancing therapeutic development. We have launched an open-source web resource, featuring this integrated map to aid the scientific community in further exploration of these data and hypothesis generation (available at http://pscb.stjude.org). Citation Format: Rich Chapple, Xueying Liu, Sivaraman Natarajan, Margaret I. Alexander, Yuna Kim, Anand Patel, Christy W. LaFlamme, Min Pan, William C. Wright, Hyeong-Min Lee, Yinwen Zhang, Meifen Lu, Selene C. Koo, Courtney Long, John Harper, Chandra Savage, Melissa D. Johnson, Thomas Confer, Walter J. Akers, Michael A. Dyer, Heather Sheppard, John Easton, Paul Geeleher. A novel unsupervised machine learning model applied to neuroblastoma single-cell RNA-seq data reveals a drug-induced mesenchymal-like gene expression program [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 868.

Determination of quality classes for material extrusion additive manufacturing using image processing

Tools for implementing a systematic quality management are necessary for the use of material extrusion as an additive manufacturing process for products with high quality requirements. Well-defined quality classes are crucial for ensuring that the requirements for a product can be communicated transparently and that the existing properties can be evaluated. Furthermore, there is a lack of capable measurement equipment for the acquisition of process data during the production process. To address these challenges, the present paper introduces an image processing system that determines quality indicators for individual layers in terms of imperfect surface percentages and the number of imperfections. The central element of the hardware is an adaptive darkfield illumination, which leads to high-contrast images. In addition, five types of layer subareas are identified in a segmentation step. Unsupervised machine learning methods are then used to detect imperfections in each layer subarea. In the segmentation, the current layer can be distinguished from irrelevant image background regions with an F-measure of 0.981. For the layer-wise measurement of the quality indicators, relative measurement errors with standard deviations of 25 to 76.1% are found. After evaluating the capabilities of the image processing system, a proposal for limits of quality classes is derived by monitoring several material extrusion processes. For this purpose, three quality classes for each of the five layer subareas are deduced from the process scatter measured by the image processing system. The results are an important contribution to the industrialization of material extrusion in safety–critical areas such as medical technology or the aerospace industry.

Optimization of number and range of shunt valve performance levels in infant hydrocephalus: a machine learning analysis.

Shunt surgery is the main treatment modality for hydrocephalus, the leading cause of brain surgery in children. The efficacy of shunt surgery, particularly in infant hydrocephalus, continues to present serious challenges in achieving improved outcomes. The crucial role of correct adjustments of valve performance levels in shunt outcomes has been underscored. However, there are discrepancies in the performance levels of valves from different companies. This study aims to address this concern by optimizing both the number and range of valve performance levels for infant hydrocephalus, aiming for improved shunt surgery outcomes. We conducted a single-center cohort study encompassing infant hydrocephalus cases that underwent initial shunt surgery without subsequent failure or unimproved outcomes. An unsupervised hierarchical machine learning method was utilized for clustering and reporting the valve drainage pressure values for all patients within each identified cluster. The optimal number of clusters corresponds to the number of valve performance levels, with the valve drainage pressure ranges within each cluster indicating the pressure range for each performance level. Comparisons based on the Silhouette coefficient between 3-7 clusters revealed that this coefficient for the 4-cluster (4-performance level) was at least 28.3% higher than that of other cluster formations in terms of intra-cluster similarity. The Davies-Bouldin index for the 4-performance level was at least 37.2% lower than that of other configurations in terms of inter-cluster dissimilarity. Cluster stability, indicated by a Jaccard index of 71% for the 4-performance level valve, validated the robustness, reliability, and repeatability of our findings. Our suggested optimized drainage pressure ranges for each performance level (1.5-5.0, 5.0-9.0, 9.0-15.0, and 15.0-18.0cm H2O) may potentially assist neurosurgeons in improving clinical outcomes for patients with shunted infantile hydrocephalus.

Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

AbstractModeling and evaluation of a system reliability is a key issue in shock model analysis. In cumulative shock model, a system fails when the total damages caused by shocks exceeds the system failure threshold level. The reliability of a system with random failure threshold level under cumulative shocks is investigated in this work, considering the general dependency between inter‐arrival times of shocks and their damages magnitudes. Clustering, as one of the unsupervised machine learning methods, is utilized here by applying a genetic algorithm (GA) and particle swarm optimization algorithm (PSO) for its implementation. Phase‐type (PH) distributions and their properties are also applied for finding a system failure distribution functions, determining the convolutions, evaluating the integrals, and ultimately modeling the system reliability. An illustrative example is provided to demonstrate the implementation and effectiveness of the proposed modeling procedure and the suggested approach for computing the system reliability and remaining useful lifetime (RUL). Also, an example case study of X‐ray machine in healthcare industry is provided to demonstrate the application of the proposed model. Applying other machine learning methods for patterns recognition is an area for future research.

Machine learning identifies fatigue as a key symptom of fibromyalgia reflected in tyrosine, purine, pyrimidine, and glutaminergic metabolism.

Fibromyalgia patients vary in clinical phenotype and treatment can be challenging. The pathophysiology of fibromyalgia is incompletely understood but appears to involve metabolic changes at rest or in response to stress. We enrolled 54 fibromyalgia patients and 31 healthy controls to this prospective study. Symptoms were assessed using the Fibromyalgia Impact Questionnaire (FIQ) and blood samples were collected for metabolomics analysis at baseline and after an oral glucose tolerance test and a cardiopulmonary exercise test. We identified key symptoms of fibromyalgia and related them to changes in metabolic pathways with supervised and unsupervised machine learning methods. Algorithms trained with the FIQ information assigned the fibromyalgia diagnosis in new data with balanced accuracy of 88% while fatigue alone already provided the diagnosis with 86% accuracy. Supervised analyses reduced the metabolomic information from 77 to 13 key markers. With these metabolites, fibromyalgia could be identified in new cases with 79% accuracy. In addition, 5-hydroxyindole-3-acetic acid and glutamine levels correlated with the severity of fatigue. Patients differed from controls at baseline in tyrosine and purine pathways, and in the pyrimidine pathway after the stress challenges. Several key markers are involved in glutaminergic neurotransmission. This data-driven analysis highlights fatigue as a key symptom of fibromyalgia. Fibromyalgia is associated with metabolic changes which also reflect the degree of fatigue. Responses to metabolic and physical stresses result in a metabolic pattern that allows discrimination of fibromyalgia patients from controls and narrows the focus on key pathophysiological processes in fibromyalgia as treatment targets.

Dietary patterns associated with the incidence of hypertension among adult Japanese males: application of machine learning to a cohort study.

The previous studies that examined the effectiveness of unsupervised machine learning methods versus traditional methods in assessing dietary patterns and their association with incident hypertension showed contradictory results. Consequently, our aim is to explore the correlation between the incidence of hypertension and overall dietary patterns that were extracted using unsupervised machine learning techniques. Data were obtained from Japanese male participants enrolled in a prospective cohort study between August 2008 and August 2010. A final dataset of 447 male participants was used for analysis. Dimension reduction using uniform manifold approximation and projection (UMAP) and subsequent K-means clustering was used to derive dietary patterns. In addition, multivariable logistic regression was used to evaluate the association between dietary patterns and the incidence of hypertension. We identified four dietary patterns: 'Low-protein/fiber High-sugar,' 'Dairy/vegetable-based,' 'Meat-based,' and 'Seafood and Alcohol.' Compared with 'Seafood and Alcohol' as a reference, the protective dietary patterns for hypertension were 'Dairy/vegetable-based' (OR 0.39, 95% CI 0.19-0.80, P = 0.013) and the 'Meat-based' (OR 0.37, 95% CI 0.16-0.86, P = 0.022) after adjusting for potential confounding factors, including age, body mass index, smoking, education, physical activity, dyslipidemia, and diabetes. An age-matched sensitivity analysis confirmed this finding. This study finds that relative to the 'Seafood and Alcohol' pattern, the 'Dairy/vegetable-based' and 'Meat-based' dietary patterns are associated with a lower risk of hypertension among men.

Machine Learning-Derived Baseline Visual Field Patterns Predict Future Glaucoma Onset in the Ocular Hypertension Treatment Study.

The Ocular Hypertension Treatment Study (OHTS) identified risk factors for primary open-angle glaucoma (POAG) in patients with ocular hypertension, including pattern standard deviation (PSD). Archetypal analysis, an unsupervised machine learning method, may offer a more interpretable approach to risk stratification by identifying patterns in baseline visual fields (VFs). There were 3272 eyes available in the OHTS. Archetypal analysis was applied using 24-2 baseline VFs, and model selection was performed with cross-validation. Decomposition coefficients for archetypes (ATs) were calculated. A penalized Cox proportional hazards model was implemented to select discriminative ATs. The AT model was compared to the OHTS model. Associations were identified between ATs with both POAG onset and VF progression, defined by mean deviation change per year. We selected 8494 baseline VFs. Optimal AT count was 19. The highest prevalence ATs were AT9, AT11, and AT7. The AT-based prediction model had a C-index of 0.75 for POAG onset. Multivariable models demonstrated that a one-interquartile range increase in the AT5 (hazard ratio [HR] = 1.14; 95% confidence interval [CI], 1.04-1.25), AT8 (HR = 1.22; 95% CI, 1.09-1.37), AT15 (HR = 1.26; 95% CI, 1.12-1.41), and AT17 (HR = 1.17; 95% CI, 1.03-1.31) coefficients conferred increased risk of POAG onset. AT5, AT10, and AT14 were significantly associated with rapid VF progression. In a subgroup analysis by high-risk ATs (>95th percentile or <75th percentile coefficients), PSD lost significance as a predictor of POAG in the low-risk group. Baseline VFs, prior to detectable glaucomatous damage, contain occult patterns representing early changes that may increase the risk of POAG onset and VF progression in patients with ocular hypertension. The relationship between PSD and POAG is modified by the presence of high-risk patterns at baseline. An AT-based prediction model for POAG may provide more interpretable glaucoma-specific information in a clinical setting.

Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms.

Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas-vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%-but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control.