Set Of Signatures Research Articles

Are Coronal Loops Projection Effects?

We report results of an in-depth numerical investigation of three-dimensional projection effects that could influence the observed loop-like structures in an optically thin solar corona. Several archetypal emitting geometries are tested, including collections of luminous structures with circular cross sections of fixed and random size, and light-emitting structures with highly anisotropic cross sections, as well as two-dimensional stochastic current density structures generated by fully developed magnetohydrodynamic turbulence. A comprehensive set of statistical signatures is used to compare the line-of-sight (LOS) integrated emission signals predicted by the constructed numerical models with the loop profiles observed by the extreme ultraviolet telescope on board the flight 2.1 of the High-Resolution Coronal Imager (Hi-C). The results suggest that typical cross-sectional emission envelopes of the Hi-C loops are unlikely to have high eccentricity, and that the observed loops cannot be attributed to randomly oriented quasi-two-dimensional emitting structures, some of which would produce anomalously strong optical signatures due to an accidental LOS alignment, as expected in the ''coronal veil“ scenario proposed recently by Malanushenko et al. The possibility of apparent loop-like projections of very small (close to the resolution limit) or very large (comparable with the size of an active region) light-emitting sheets remains open, but the intermediate range of scales commonly associated with observed loop systems is most likely filled with true quasi-one-dimensional (roughly axisymmetric) structures embedded into the three-dimensional coronal volume.

Common signatures of selection reveal target loci for breeding across soybean populations.

Understanding the underlying genetic bases of yield-related selection and distinguishing these changes from genetic drift are critical for both improved understanding and future success of plant breeding. Soybean [Glycine max (L.) Merr.] is a key species for world food security, yet knowledge of the mechanism of selective breeding in soybean, such as the century-long program of artificial selection in U.S. soybean germplasm, is currently limited to certain genes and loci. Here, we identify genome-wide signatures of selection in separate populations of soybean subjected to artificial selection for increased yield by multiple breeding programs in the United States. We compared the alternative soybean breeding population (AGP) created by USDA-ARS to the conventional public soybean lines (CGP) developed at three different stages of breeding (ancestral, intermediate, and elite) to identify shared signatures of selection and differentiate these from drift. The results showed a strong selection for specific haplotypes identified by single site frequency and haplotype homozygosity methods. A set of common selection signatures was identified in both AGP and CGP that supports the hypothesis that separate breeding programs within similar environments coalesce on the fixation of the same key haplotypes. Signatures unique to each breeding program were observed. These results raise the possibility that selection analysis can allow the identification of favorable alleles to enhance directed breeding approaches.

Transcriptional Inflammatory Signature in Healthy Donors and Different Radiotherapy Cancer Patients.

Cancer and ionizing radiation exposure are associated with inflammation. To identify a set of radiation-specific signatures of inflammation-associated genes in the blood of partially exposed radiotherapy patients, differential expression of 249 inflammatory genes was analyzed in blood samples from cancer patients and healthy individuals. The gene expression analysis on a cohort of 63 cancer patients (endometrial, head and neck, and prostate cancer) before and during radiotherapy (24 h, 48 h, ~1 week, ~4-8 weeks, and 1 month after the last fraction) identified 31 genes and 15 up- and 16 down-regulated genes. Transcription variability under normal conditions was determined using blood drawn on three separate occasions from four healthy donors. No difference in inflammatory expression between healthy donors and cancer patients could be detected prior to radiotherapy. Remarkably, repeated sampling of healthy donors revealed an individual endogenous inflammatory signature. Next, the potential confounding effect of concomitant inflammation was studied in the blood of seven healthy donors taken before and 24 h after a flu vaccine or ex vivo LPS (lipopolysaccharide) treatment; flu vaccination was not detected at the transcriptional level and LPS did not have any effect on the radiation-induced signature identified. Finally, we identified a radiation-specific signature of 31 genes in the blood of radiotherapy patients that were common for all cancers, regardless of the immune status of patients. Confirmation via MQRT-PCR was obtained for BCL6, MYD88, MYC, IL7, CCR4 and CCR7. This study offers the foundation for future research on biomarkers of radiation exposure, radiation sensitivity, and radiation toxicity for personalized radiotherapy treatment.

Parallelized Inter-Image k-Means Clustering Algorithm for Unsupervised Classification of Series of Satellite Images

As the volume of satellite images increases rapidly, unsupervised classification can be utilized to swiftly investigate land cover distributions without prior knowledge and to generate training data for supervised (or deep learning-based) classification. In this study, an inter-image k-means clustering algorithm (IIkMC), as an improvement of the native k-means clustering algorithm (kMC), was introduced to obtain a single set of class signatures so that the classification results could be compatible among multiple images. Because IIkMC was a computationally intensive algorithm, parallelized approaches were deployed, using multi-cores of a central processing unit (CPU) and a graphics processing unit (GPU), to speed up the process. kMC and IIkMC were applied to a series of images acquired in a PlanetScope mission. In addition to the capability of the inter-image compatibility of the classification results, IIkMC could settle the problem of incomplete segmentation and class canceling revealed in kMC. Based on CPU parallelism, the speed of IIkMC improved, becoming up to 12.83 times better than sequential processing. When using a GPU, the speed improved up to 25.53 times, rising to 39.00 times with parallel reduction. From the results, it was confirmed IIkMC provided more reliable results than kMC, and its parallelism could facilitate the overall inspection of multiple images.

INDICATORS OF INFORMATION FEATURES FOR RECOGNISING THE STATE OF SOURCES AND OBJECTS OF TELECOMMUNICATION NETWORKS AND SYSTEMS

Background. The majority of modern procedures for the recognition of radio sources and objects are based on the use of binary and multivalued logic, which have low specific features. The essence of the issues is to compare a priori knowledge and a posteriori data coming from the surveillance means and to make a decision on the recognition of a radio emission object. A priori knowledge and a posteriori data are formed both before and during the recognition process on the basis of sets of information features or information signatures. At the same time, when constructing an integral indicator for determining the affiliation and status of sources and objects, it is necessary to know the weighting coefficients of information features, the determination of which is a rather difficult task. Therefore, the issue of determining the weighting coefficients that characterise information features remains an urgent task in the field of statistical radio engineering.
 Objective. The purpose of the paper is to select and substantiate a simple and effective method for calculating the weighting coefficients of information features for the implementation of the methodology for recognising radio sources and objects.
 Methods. Decision-making on the value of the weighting coefficients of information features of the recognition objects belonging to a certain class is based on the results of calculations using one of the three Fishburne formulae, which, in comparison with the known methods of expert assessments, are very simple and understandable, do not require any additional research and complex calculations.
 Results. The procedure is proposed and an example of using the Fishburne method (three formulae) in calculating the value of the weighting coefficients of information features for recognising sources and objects of radio monitoring is considered.
 Conclusions. Comparison of the method of calculating the weighting coefficients using Fishburne's formulae with other known methods of expert assessments shows that there is no need to interview experts and process their analysis results; there are no restrictive implementation conditions; it is easy to take into account additional information about the indicators, if necessary; no software implementation with a complex search algorithm is required; it is easy to make any changes as additional information indicators.

Metabolic signatures of tear extracellular vesicles caused by herpes simplex keratitis

PurposeHerpes simplex keratitis (HSK), caused by type 1 herpes simplex virus (HSV) reactivation, is a severe infectious disease that leads to vision loss. HSV can trigger metabolic reprogramming in the host cell and change the extracellular vesicles (EV) cargos; however, little is known about the EV metabolic signatures during ocular HSV infection. Here, we aimed to depict the EV-associated metabolic landscape in HSK patients’ tears. MethodsWe collected 82 samples from 41 participants with unilateral HSK (contralateral unaffected tears were set as negative control), including subtype cohorts of 13 epithelial, 20 stromal, and 8 endothelial HSK. We isolated tear EVs via our previously established platform and conducted metabolic analysis using LC-MS/MS. The metabolic signatures for recognizing HSK and subtypes were assessed through differential analysis and machine learning algorithms. ResultsHypopsia and increased extracellular CD63 levels were observed in affected eyes. We identified 339 metabolites based on sEVs isolated from tears. Differential analysis revealed alterations in energy and amino acid metabolism, as well as the infectious microenvironment. Furthermore, we observed dysregulated metabolite such as methyldopa, which is associated with inappropriate neovascularization and corneal sensation loss, contributing to the HSK severity particularly in the stromal subtype. Moreover, machine learning classification also suggested a set of EV metabolic signatures that have potential for pan-keratitis detection. ConclusionsOur findings demonstrate that tear EV metabolites can serve as valuable indicators for comprehending the underlying pathological mechanisms. This knowledge is expected to facilitate the development of liquid biopsy means and therapeutic target discovery.

The Algebra of Signatures for Extreme Two-Uniform Hypergraphs

In the last decade, several characterizations have been constructed for constructions such as extreme hypergraphs. One of the most recently described features is the signature. A signature is a number that uniquely describes an extremal and allows one to efficiently store the extremal two-uniform hypergraph itself. However, for the signature, although various algorithms have been derived for transforming it into other object-characteristics such as the base, the adjacency matrix, and the vector of vertex degrees, no isolated signature union and intersection apparatus has been constructed. This allows us to build efficient algorithms based on signatures, the most compact representation of extremal two-uniform hypergraphs. The nature of the algebraic construction that can be built on a set of signatures using union and intersection operations has also been defined. It is proved that an algebra on a set of signatures with either the union or intersection operation forms a monoid; if the algebra is defined on a set of signatures with both union and intersection operations, it forms a distributive lattice.

Deep convolutional transformer network for hyperspectral unmixing

ABSTRACT Hyperspectral unmixing (HU) is considered one of the most important ways to improve hyperspectral image analysis. HU aims to break down the mixed pixel into a set of spectral signatures, often commonly referred to as endmembers, and determine the fractional abundance of those endmembers. Deep learning (DL) approaches have recently received great attention regarding HU. In particular, convolutional neural networks (CNNs)-based methods have performed exceptionally well in such tasks. However, the ability of CNNs to learn deep semantic features is limited, and computing cost increase dramatically with the number of layers. The appearance of the transformer addresses these issues by effectively representing high-level semantic features well. In this article, we present a novel approach for HU that utilizes a deep convolutional transformer network. Firstly, the CNN-based autoencoder (AE) is used to extract low-level features from the input image. Secondly, the concept of tokenizer is applied for feature transformation. Thirdly, the transformer module is used to capture the deep semantic features derived from the tokenizer. Finally, a convolutional decoder is utilized to reconstruct the input image. The experimental results on synthetic and real datasets demonstrate the effectiveness and superiority of the proposed method compared with other unmixing methods.

OR08-02 In Situ Reconstruction Of Distinct Normal And Pathological Cell Populations Within The Human Adrenal Gland Using Spatial Transcriptomics

Abstract Disclosure: F. Rui: None. K. Walters: None. K. Koc: None. A. Baldwin: Stock Owner; Self; Tempus Labs. K. Kiseljak-Vassiliades: Advisory Board Member; Self; HRA Pharma. L.M. Fishbein: Consulting Fee; Self; Lantheus/Progenics Azedra. N. Mukherjee: None. The human adrenal gland is a vital endocrine organ controlling a multitude of functions through the secretion of cortical steroid and medullary catecholamine hormones. Several outstanding questions remain about the adrenal spatial organization and related differential developmental signaling pathways which have functional consequences within and between the cortex and medulla. The current model of the functional zonation of the cortex posits a centripetal differentiation process in which the cortical cells arise in the capsule and migrate inwards changing their gene expression patterns as they progress to the three different functional layers of the cortex. The evidence for this model is based on mouse lineage tracing experiments, and it is unclear to what extent it applies to human adrenal glands. To address this, we performed spatially resolved transcriptomics on four adult adrenal sections from two different human specimens to identify cell populations and their spatial relationship in this highly organized and functionally diverse organ. We identified 14 distinct cell populations; 79% of these spots represented the cortex or medulla and exhibited the concentric expression pattern reflecting the known spatial organization of the human adrenal gland. Trajectory inference analysis of the cortical cells showed continuous expression transitions from cells of the capsule to zona glomerulosa to zona fasciculata to zona reticularis. These data demonstrate for the first time that the centripetal differentiation model for functional zonation in mice is consistent for adult human adrenal tissue. We also validated the spatial expression pattern of a subset of WNT-related genes specifically expressed with distinct patterns in the capsule and zona glomerulosa cells. These genes represent novel regulatory factors that may be crucial in the differentiation of adult human adrenal cortex. Finally, we observed two CYP11B2 positive aldosterone producing cell clusters (APCC) in the adrenal tissue section from a 72-year-old woman. APCCs are thought to play a role in the pathological progression of primary aldosteronism; however, it is unclear if there are unifying expression signatures other than CYP11B2 protein expression. Our analysis found both similarities and differences to previously published data sets of APCC signatures. It is unclear to what extent these expression differences are technical (i.e. different platforms) or due to biological heterogeneity (between individuals or APCCs). The subgroup of similar gene expression in APCCs across all studies thus far may represent a core APCC signature which should be validated on a larger cohort. Altogether, our study uses spatial transcriptomics of the normal human adrenal and the results have important implications for the differentiation and functional zonation,as well as, for the early dysregulation leading to primary aldosteronism. Presentation: Friday, June 16, 2023

Ensemble of Artificial Neural Networks for Approximating the Survival Signature of Critical Infrastructures

Abstract Survival signature can be useful for the reliability assessment of critical infrastructures. However, analytical calculation and Monte Carlo Simulation (MCS) are not feasible for approximating the survival signature of large infrastructures, because of the complexity and computational demand due to the large number of components. In this case, efficient and accurate approximations are sought. In this paper we formulate the survival signature approximation problem as a missing data problem. An ensemble of artificial neural networks (ANNs) is trained on a set of survival signatures obtained by MCS. The ensemble of trained ANNs is, then, used to retrieve the missing values of the survival signature. A numerical example is worked out and recommendations are given to design the ensemble of ANNs for large-scale, real-world infrastructures. The electricity grid of Great Britain, the New England power grid (IEEE 39-Bus Case), the reduced Berlin metro system and the approximated American Power System (IEEE 118-Bus Case) are, then, eventually, analyzed as particular case studies.

Somatic structural variation signatures in pediatric brain tumors

Brain cancer is the leading cause of cancer-related death in children. Somatic structural variations (SVs), large-scale alterations in DNA, remain poorly understood in pediatric brain tumors. Here, we detect a total of 13,199 high-confidence somatic SVs in 744 whole-genome sequences of pediatric brain tumors from the Pediatric Brain Tumor Atlas. The somatic SV occurrences have tremendous diversity among the cohort and across different tumor types. We decompose mutational signatures of clustered complex SVs, non-clustered complex SVs, and simple SVs separately to infer their mutational mechanisms. Our finding of many tumor types carrying unique sets of SV signatures suggests that distinct molecular mechanisms shape genome instability in different tumor types. The patterns of somatic SV signatures in pediatric brain tumors are substantially different from those in adult cancers. The convergence of multiple SV signatures on several major cancer driver genes implies vital roles of somatic SVs in disease progression.

A transcriptome study of p53-pathway related prognostic gene signature set in bladder cancer

p53 pathway is important in tumorigenesis. However, no study has been performed to specifically investigate the role of p53 pathway genes in bladder cancer (BLCA). In this study, transcriptomics data of muscle invasive bladder cancer patients (n = 411) from The Cancer Genome Atlas (TCGA) were investigated. Using the hallmark p53 pathway gene set, the Non-Negative Matrix factorization (NMF) analysis identified two subtypes (C1 and C2). Clinical, survival, and immunological analysis were done to validate distinct characteristics of the subtypes. Pathway enrichment analysis showed the subtype C1 with poor prognosis having enrichment in genes of the immunity related pathways, where C2 subtype with better prognosis being enriched in genes of the steroid synthesis and drug metabolism pathways. A signature gene set consisting of MDGA2, GNLY, GGT2, UGT2B4, DLX1, and DSC1 was created followed by a risk model. Their expressions were analyzed in RNA extracted from the blood and matched tumor tissues of BLCA patients (n = 10). DSC1 had significant difference of expression (p = 0.005) between the blood and tumor tissues in our BLCA samples. Contrary to the usual normal bladder tissue to blood ratio, DLX1 expression was lower (p = 0.02734) in tumor tissues than in blood. Being the first research of p53 pathway related signature gene set in bladder cancer, this study potentially has a substantial impact on the development of biomarkers for BLCA.

DDI-SSL: Drug–Drug Interaction Prediction Based on Substructure Signature Learning

Drug–drug interactions (DDIs) are entities composed of different chemical substructures (functional groups). In existing methods that predict drug–drug interactions based on the usage of substructures, each node is perceived as the epicenter of a sub-pattern, and adjacent nodes eventually become centers of similar substructures, resulting in redundancy. Furthermore, the significant differences in structure and properties among compounds can lead to unrelated pairings, making it difficult to integrate information. This heterogeneity negatively affects the prediction results. In response to these challenges, we propose a drug–drug interaction prediction method based on substructure signature learning (DDI-SSL). This method extracts useful information from local subgraphs surrounding drugs and effectively utilizes substructures to assist in predicting drug side effects. Additionally, a deep clustering algorithm is used to aggregate similar substructures, allowing any individual subgraph to be reconstructed using this set of global signatures. Furthermore, we developed a layer-independent collaborative attention mechanism to model the mutual influence between drugs, generating signal strength scores for each class of drugs to mitigate noise caused by heterogeneity. Finally, we evaluated DDI-SSL on a comprehensive dataset and demonstrated improved performance in DDI prediction compared to state-of-the-art methods.

Mutational signature assignment heterogeneity is widespread and can be addressed by ensemble approaches.

Single-base substitution (SBS) mutational signatures have become standard practice in cancer genomics. In lieu of de novo signature extraction, reference signature assignment allows users to estimate the activities of pre-established SBS signatures within individual malignancies. Several tools have been developed for this purpose, each with differing methodologies. However, due to a lack of standardization, there may be inter-tool variability in signature assignment. We deeply characterized three assignment strategies and five SBS signature assignment tools. We observed that assignment strategy choice can significantly influence results and interpretations. Despite varying recommendations by tools, Refit performed best by reducing overfitting and maximizing reconstruction of the original mutational spectra. Even after uniform application of Refit, tools varied remarkably in signature assignments both qualitatively (Jaccard index = 0.38-0.83) and quantitatively (Kendall tau-b = 0.18-0.76). This phenomenon was exacerbated for 'flat' signatures such as the homologous recombination deficiency signature SBS3. An ensemble approach (EnsembleFit), which leverages output from all five tools, increased SBS3 assignment accuracy in BRCA1/2-deficient breast carcinomas. After generating synthetic mutational profiles for thousands of pan-cancer tumors, EnsembleFit reduced signature activity assignment error 15.9-24.7% on average using Catalogue of Somatic Mutations In Cancer and non-standard reference signature sets. We have also released the EnsembleFit web portal (https://www.ensemblefit.pittlabgenomics.com) for users to generate or download ensemble-based SBS signature assignments using any strategy and combination of tools. Overall, we show that signature assignment heterogeneity across tools and strategies is non-negligible and propose a viable, ensemble solution.

Identification of prognostic immune subtypes of lung squamous cell carcinoma by unsupervised consistent clustering.

We performed UCC on the expression data of lung squamous cell carcinoma tumor samples to identify the classification of lung squamous cell carcinoma (LUSC) tumor samples, and calculated the levels of different classified immune cells by single-sample gene enrichment analysis (ssGSEA) to obtain a set of immune-related subtype gene tags, which can be used for subtype classification of lung squamous cell carcinoma. TCGA-LUSC and GSE30219 data of lung squamous cell carcinoma were obtained from TCGA and GEO databases. Prognostic-associated subtypes were identified by unsupervised consensus clustering (UCC). Using ssGSEA analysis to calculate the level of immune cells of different subtypes, obtain the connection between subtypes and immunity, identify the gene signatures recognized by subtypes, and verify this group of gene signatures through GSE30219. We effectively identified 2 subtypes that were significantly associated with prognostic survival by UCC, and calculated according to ssGSEA, the 2 subtypes were significantly different at the level of immune cells, followed by introducing a This weighted thinking computes a set of gene signatures that are significantly associated with subtype 1. During validation, this set of gene signatures could efficiently and robustly identify distinct prognostic immune subtypes, demonstrated the validity of this set of gene signatures, as well as 2 subtypes of lung squamous cell carcinoma. We used lung squamous cell carcinoma data from public databases and identified 2 prognostic immunosubtypes of lung squamous cell carcinoma and a set of gene tags that can be used to classify immune subtypes of lung squamous cell carcinoma, which may provide effective evidence for accurate clinical treatment of lung squamous cell carcinoma.

Exploring Handwritten Signature Image Features for Hardware Security

This paper presents a novel hardware security technique that leverages handwritten signature image features for securing intellectual property (IP) cores, such as digital signal processing (DSP) cores, against IP piracy and false claim of IP ownership threats. In our approach, an IP vendor's handwritten signature image features are first converted into a corresponding digital template, followed by mapping into hardware security constraints and implanting them into the design during high level synthesis (HLS) process. This paper presents methodologies of extracting feature set of a handwritten signature through sampling and of encoding of the samples into binary values using a tree based encoding, for generating the digital template. The results of the proposed approach are assessed in terms of strength of IP ownership proof, security against a forged signature and impact of embedding signature constraints on design cost. The results revealed that the proposed approach provides robust security at negligible design cost overhead and also outperforms state of the art hardware security approaches for DSP cores.

Crystal: Enhancing Blockchain Mining Transparency With Quorum Certificate

Researchers have discovered a series of theoretical attacks against Bitcoin's Nakamoto consensus; the most damaging ones are selfish mining, double-spending, and consistency delay attacks. These attacks have one common cause: block withholding. This paper proposes Crystal, which leverages quorum certificates to resist block withholding misbehavior. Crystal continuously elects committees from miners and requires each block to have a quorum certificate, i.e., a set of signatures issued by members of its committee. Consequently, an attacker has to publish its blocks to obtain quorum certificates, rendering block withholding impossible. To build Crystal, we design a novel two-round committee election in a Sybil-resistant, unpredictable and non-interactive way, and a reward mechanism to incentivize miners to follow the protocol. Our analysis and evaluations show that Crystal can significantly mitigate selfish mining and double-spending attacks. For example, in Bitcoin, an attacker with 30% of the total computation power will succeed in double-spending attacks with a probability of 15.6% to break the 6-confirmation rule; however, in Crystal, the success probability for the same attacker falls to 0.62%. We provide formal end-to-end safety proofs for Crystal, ensuring no unknown attacks will be introduced. To the best of our knowledge, Crystal is the first protocol that prevents selfish mining and double-spending attacks while providing safety proof.

Loop Aborts Strike Back: Defeating Fault Countermeasures in Lattice Signatures with ILP

At SAC 2016, Espitau et al. presented a loop-abort fault attack against lattice-based signature schemes following the Fiat–Shamir with aborts paradigm. Their attack recovered the signing key by injecting faults in the sampling of the commitment vector (also called masking vector) y, leaving its coefficients at their initial zero value. As possible countermeasures, they proposed to carry out the sampling of the coefficients of y in shuffled order, or to ensure that the masking polynomials in y are not of low degree. In this paper, we show that both of these countermeasures are insufficient. We demonstrate a new loop-abort fault injection attack against Fiat–Shamir with aborts lattice-based signatures that can recover the secret key from faulty signatures even when the proposed countermeasures are implemented. The key idea of our attack is that faulted signatures give rise to a noisy linear system of equations, which can be solved using integer linear programming. We present an integer linear program that recovers the secret key efficiently in practice, and validate the efficacy of our attack by conducting a practical end-to-end attack against a shuffled version of the Dilithium reference implementation, mounted on an ARM Cortex M4. We achieve a full (equivalent) key recovery in under 3 minutes total execution time (including signature generation), using only 5 faulted signatures. In addition, we conduct extensive theoretical simulations of the attack against Dilithium. We find that our method can achieve key recovery in under 5 minutes given a (sufficiently large) set of signatures where just one of the coefficients of y is zeroed out (or left at its initial value of zero). Furthermore, we find that our attack works against all security levels of Dilithium. Our attack shows that protecting Fiat–Shamir with aborts lattice-based signatures against fault injection attacks cannot be achieved using the simple countermeasures proposed by Espitau et al. and likely requires significantly more expensive countermeasures.

Lower serum branched-chain amino acid catabolic intermediates are predictive signatures specific to patients with diabetic foot

Diabetic foot (DF) is one of the serious chronic complications of diabetes. Accurate prediction of the risk of DF may take timely intervention measures to prevent its occurrence. The understanding of metabolomic changes in the progression of diabetes to DF may reveal new targets for interventions. We hypothesized that changes in metabolic pathways during DF would lead to changes in the metabolic profile, which could be predictive signature specific to it. In the present study, 43 participants with type 2 diabetes mellitus (T2DM), 32 T2DM participants with DF (T2DM-F), and 36 healthy subjects were enrolled and their serum samples were used for targeted and nonpolar metabolic analysis with liquid chromatography-tandem mass spectrometry. Differential metabolites related to T2DM-F were discovered in metabolomic analysis. Lasso machine learning regression model, random forest algorithm, causal mediation analysis, disease risk assessment, and clinical decision model were carried out. T2DM and T2DM-F groups could be distinguished with the healthy control group. The differential metabolites were all enriched in alpha-linolenic acid and linoleic acid metabolic pathways including arachidonic acid, docosapentaenoic-acid 22N-6, and docosahexaenoic-acid, which were significantly lower in the T2DM and T2DM-F groups compared with the healthy control group. The differential metabolites in T2DM-F vs T2DM groups were enriched to branched-chain amino acid (BCAA) catabolic pathways involving in methylmalonic acid, succinic acid, 3-methyl-2-oxovaleric acid, and ketoleucine, which were the BCAA catabolic intermediates and significantly lower in the T2DM-F compared with the T2DM group except for succinic acid. We reveal a new set of predictive signatures and associate the lower BCAA catabolic intermediates with the progression from T2DM to T2DM-F.

Immune cell type signature discovery and random forest classification for analysis of single cell gene expression datasets.

Robust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of multiple integrated datasets could lead to high-quality immune cell signatures which could be used as superior inputs to machine learning-based cell type classification approaches. We established a novel workflow for the discovery of immune cell type signatures based primarily on gene-versus-gene expression similarity. It leverages multiple datasets, here seven single cell expression datasets from six different cancer types and resulted in eleven immune cell type-specific gene expression signatures. We used these to train random forest classifiers for immune cell type assignment for single-cell RNA-seq datasets. We obtained similar or better prediction results compared to commonly used methods for cell type assignment in independent benchmarking datasets. Our gene signature set yields higher prediction scores than other published immune cell type gene sets in random forest-based cell type classification. We further demonstrate how our approach helps to avoid bias in downstream statistical analyses by re-analysis of a published IFN stimulation experiment. We demonstrated the quality of our immune cell signatures and their strong performance in a random forest-based cell typing approach. We argue that classifying cells based on our comparably slim sets of genes accompanied by a random forest-based approach not only matches or outperforms widely used published approaches. It also facilitates unbiased downstream statistical analyses of differential gene expression between cell types for significantly more genes compared to previous cell classification algorithms.