Independent Set Research Articles

Leveraging Serial Low-Dose CT Scans in Radiomics-based Reinforcement Learning to Improve Early Diagnosis of Lung Cancer at Baseline Screening.

Purpose To evaluate the feasibility of leveraging serial low-dose CT (LDCT) scans to develop a radiomics-based reinforcement learning (RRL) model for improving early diagnosis of lung cancer at baseline screening. Materials and Methods In this retrospective study, 1951 participants (female patients, 822; median age, 61 years [range, 55-74 years]) (male patients, 1129; median age, 62 years [range, 55-74 years]) were randomly selected from the National Lung Screening Trial between August 2002 and April 2004. An RRL model using serial LDCT scans (S-RRL) was trained and validated using data from 1404 participants (372 with lung cancer) containing 2525 available serial LDCT scans up to 3 years. A baseline RRL (B-RRL) model was trained with only LDCT scans acquired at baseline screening for comparison. The 547 held-out individuals (150 with lung cancer) were used as an independent test set for performance evaluation. The area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI) were used to assess the performances of the models in the classification of screen-detected nodules. Results Deployment to the held-out baseline scans showed that the S-RRL model achieved a significantly higher test AUC (0.88 [95% CI: 0.85, 0.91]) than both the Brock model (AUC, 0.84 [95% CI: 0.81, 0.88]; P = .02) and the B-RRL model (AUC, 0.86 [95% CI: 0.83, 0.90]; P = .02). Lung cancer risk stratification was significantly improved by the S-RRL model as compared with Lung CT Screening Reporting and Data System (NRI, 0.29; P < .001) and the Brock model (NRI, 0.12; P = .008). Conclusion The S-RRL model demonstrated the potential to improve early diagnosis and risk stratification for lung cancer at baseline screening as compared with the B-RRL model and clinical models. Keywords: Radiomics-based Reinforcement Learning, Lung Cancer Screening, Low-Dose CT, Machine Learning © RSNA, 2024 Supplemental material is available for this article.

An automated hybrid approach via deep learning and radiomics focused on the midbrain and substantia nigra to detect early-stage Parkinson’s disease

ObjectivesThe altered neuromelanin in substantia nigra pars compacta (SNpc) is a valuable biomarker in the detection of early-stage Parkinson’s disease (EPD). Diagnosis via visual inspection or single radiomics based method is challenging. Thus, we proposed a novel hybrid model that integrates radiomics and deep learning methodologies to automatically detect EPD based on neuromelanin-sensitive MRI, namely short-echo-time Magnitude (setMag) reconstructed from quantitative susceptibility mapping (QSM).MethodsIn our study, we collected QSM images including 73 EPD patients and 65 healthy controls, which were stratified into training-validation and independent test sets with an 8:2 ratio. Twenty-four participants from another center were included as the external validation set. Our framework began with the detection of the brainstem utilizing YOLO-v5. Subsequently, a modified LeNet was applied to obtain deep learning features. Meanwhile, 1781 radiomics features were extracted, and 10 features were retained after filtering. Finally, the classified models based on radiomics features, deep learning features, and the hybrid of both were established through machine learning algorithms, respectively. The performance was mainly evaluated using accuracy, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). The saliency map was used to visualize the model.ResultsThe hybrid feature-based support vector machine (SVM) model showed the best performance, achieving ACC of 96.3 and 95.8% in the independent test set and external validation set, respectively. The model established by hybrid features outperformed the one radiomics feature-based (NRI: 0.245, IDI: 0.112). Furthermore, the saliency map showed that the bilateral “swallow tail” sign region was significant for classification.ConclusionThe integration of deep learning and radiomic features presents a potent strategy for the computer-aided diagnosis of EPD. This study not only validates the accuracy of our proposed model but also underscores its interpretability, evidenced by differential significance across various anatomical sites.

Assessing the Utility of a Genotype-Guided Tacrolimus Equation in African American Kidney Transplant Recipients: A Single Institution Retrospective Study.

Tacrolimus metabolism is heavily influenced by the CYP3A5 genotype, which varies widely among African Americans (AA). We aimed to assess the performance of a published genotype-informed tacrolimus dosing model in an independent set of adult AA kidney transplant (KTx) recipients. CYP3A5 genotypes were obtained for all AA KTx recipients (n = 232) from 2010 to 2019 who met inclusion criteria at a single transplant center in Philadelphia, Pennsylvania, USA. Medical record data were used to calculate predicted tacrolimus clearance using the published AA KTx dosing equation and two modified iterations. Observed and model-predicted trough levels were compared at 3 days, 3 months, and 6 months post-transplant. The mean prediction error at day 3 post-transplant was 3.05 ng/mL, indicating that the model tended to overpredict the tacrolimus trough. This bias improved over time to 1.36 and 0.78 ng/mL at 3 and 6 months post-transplant, respectively. Mean absolute prediction error-a marker of model precision-improved with time to 2.33 ng/mL at 6 months. Limiting genotype data in the model decreased bias and improved precision. The bias and precision of the published model improved over time and were comparable to studies in previous cohorts. The overprediction observed by the published model may represent overfitting to the initial cohort, possibly limiting generalizability.

Intraoperative spectroscopic evaluation of sentinel lymph nodes in breast cancer surgery.

Sentinel lymph node (SLN) biopsy is a standard procedure for patients with breast cancer and normal axilla on imaging. Positive SLNs on histological examination can lead to a subsequent surgery for axillary lymph node clearance (ALNC). Here we report a non-destructive technique based on autofluorescence (AF) imaging and Raman spectroscopy for intra-operative assessment of SLNs excised in breast cancer surgery. A microscope integrating AF imaging and Raman spectroscopy modules was built to allow scanning of lymph node biopsy samples. During AF-Raman measurements, AF imaging determined optimal sampling locations for Raman spectroscopy measurements. After optimisation of the AF image analysis and training of classification models based on data from 85 samples, the AF-Raman technique was tested on an independent set of 81 lymph nodes comprising 58 fixed and 23 fresh specimens. The sensitivity and specificity of AF-Raman were calculated using post-operative histology as a standard of reference. The independent testset contained 66 negative lymph nodes and 15 positive lymph nodes according to the reference standard, collected from 78 patients. For this set of specimens, the area under the receiver operating characteristic (ROC) curve for the AF-Raman technique was 0.93 [0.83-0.98]. AF-Raman was then operated in a regime that maximised detection specificity, producing a 94% detection accuracy: 80% sensitivity and 97% specificity. The main confounders for SLN metastasis were areas rich in histiocytes clusters, for which only few Raman spectra had been included in the training dataset. This preliminary study indicates that with further development and extension of the training dataset by inclusion of additional Raman spectra of histiocytes clusters and capsule, the AF-Raman may become a promising technique for intra-operative assessment of SLNs. Intra-operative detection of positive biopsies could avoid second surgery for axillary clearance.

A longer time to relapse is associated with a larger increase in differences between paired primary and recurrent IDH wild-type glioblastomas at both the transcriptomic and genomic levels

Glioblastoma (GBM) is the most common malignant brain tumor in adults, which remains incurable and often recurs rapidly after initial therapy. While large efforts have been dedicated to uncover genomic/transcriptomic alternations associated with the recurrence of GBMs, the evolutionary trajectories of matched pairs of primary and recurrent (P-R) GBMs remain largely elusive. It remains challenging to identify genes associated with time to relapse (TTR) and construct a stable and effective prognostic model for predicting TTR of primary GBM patients. By integrating RNA-sequencing and genomic data from multiple datasets of patient-matched longitudinal GBMs of isocitrate dehydrogenase wild-type (IDH-wt), here we examined the associations of TTR with heterogeneities between paired P-R GBMs in gene expression profiles, tumor mutation burden (TMB), and microenvironment. Our results revealed a positive correlation between TTR and transcriptomic/genomic differences between paired P-R GBMs, higher percentages of non-mesenchymal-to-mesenchymal transition and mesenchymal subtype for patients with a short TTR than for those with a long TTR, a high correlation between paired P-R GBMs in gene expression profiles and TMB, and a negative correlation between the fitting level of such a paired P-R GBM correlation and TTR. According to these observations, we identified 55 TTR-associated genes and thereby constructed a seven-gene (ZSCAN10, SIGLEC14, GHRHR, TBX15, TAS2R1, CDKL1, and CD101) prognostic model for predicting TTR of primary IDH-wt GBM patients using univariate/multivariate Cox regression analyses. The risk scores estimated by the model were significantly negatively correlated with TTR in the training set and two independent testing sets. The model also segregated IDH-wt GBM patients into two groups with significantly divergent progression-free survival outcomes and showed promising performance for predicting 1-, 2-, and 3-year progression-free survival rates in all training and testing sets. Our findings provide new insights into the molecular understanding of GBM progression at recurrence and potential targets for therapeutic treatments.

Spectral Extremal Results on Trees

Let ${\rm spex}(n,F)$ be the maximum spectral radius over all $F$-free graphs of order $n$, and ${\rm SPEX}(n,F)$ be the family of $F$-free graphs of order $n$ with spectral radius equal to ${\rm spex}(n,F)$. Given integers $n,k,p$ with $n&gt;k&gt;0$ and $0\leq p\leq \lfloor(n-k)/2\rfloor$, let $S_{n,k}^{p}$ be the graph obtained from $K_k\nabla(n-k)K_1$ by embedding $p$ independent edges within its independent set, where '$\nabla$' means the join product. For $n\geq\ell\geq 4$, let $G_{n,\ell}=S_{n,(\ell-2)/2}^{0}$ if $\ell$ is even, and $G_{n,\ell}=S_{n,(\ell-3)/2}^{1}$ if $\ell$ is odd. Cioabă, Desai and Tait [SIAM J. Discrete Math. 37 (3) (2023) 2228-2239] showed that for $\ell\geq 6$ and sufficiently large $n$, if $\rho(G)\geq \rho(G_{n,\ell})$, then $G$ contains all trees of order $\ell$ unless $G=G_{n,\ell}$. They further posed a problem to study ${\rm spex}(n,F)$ for various specific trees $F$. Fix a tree $F$ of order $\ell\geq 6$, let $A$ and $B$ be two partite sets of $F$ with $|A|\leq |B|$, and set $q=|A|-1$. We first show that any graph in ${\rm SPEX}(n,F)$ contains a spanning subgraph $K_{q,n-q}$ for $q\geq 1$ and sufficiently large $n$. Consequently, $\rho(K_{q,n-q})\leq {\rm spex}(n,F)\leq \rho(G_{n,\ell})$, we further respectively characterize all trees $F$ with these two equalities holding. Secondly, we characterize the spectral extremal graphs for some specific trees and provide asymptotic spectral extremal values of the remaining trees. In particular, we characterize the spectral extremal graphs for all spiders, surprisingly, the extremal graphs are not always the spanning subgraph of $G_{n,\ell}$.

Inferring Independent Sets of Gaussian Variables after Thresholding Correlations

We consider testing whether a set of Gaussian variables, selected from the data, is independent of the remaining variables. This set is selected via a very simple approach: these are the variables for which the correlation with all other variables falls below some threshold. Unlike other settings in selective inference, failure to account for the selection step leads to excessively conservative (as opposed to anti-conservative) results. We propose a new test that conditions on the event that the selection resulted in the set of variables in question, and thus is not overly conservative. To achieve computational tractability, we develop a characterization of the conditioning event in terms of the canonical correlation between groups of random variables. In simulation studies and in the analysis of gene co-expression networks, we show that our approach has much higher power than a “naive” approach that ignores the effect of selection. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Abstract PR009: Exploring the functional consequences of APOBEC3-induced non-coding hotspot mutations in bladder cancer using massively parallel reporter assays and CRISPR-mediated base editing

Abstract Cancer research has traditionally focused on protein-coding genes, but approximately 98% of the human genome is non-coding, comprising gene regulatory elements such as enhancers, silencers, and promoters. Mutations within these regions can foster tumorigenesis by modulating the expression of oncogenes. Recent whole-genome sequencing studies have revealed several recurrently mutated non-coding genomic regions, including in bladder cancer, but their role in tumorigenesis remains unknown. In this study, we delved into whole-genome sequencing data from the bladder cancer TCGA cohort and an independent set of bladder tumors, uncovering hundreds of recurrent mutations. Mutational signature analysis identified over 65% of these mutations as APOBEC3-induced. We substantiated the involvement of APOBEC3 enzymes by using conventional deamination assays and novel approach dubbed as APOBEC3-mediated mutation capture assay. Predominantly, these mutations occurred in the hairpin loop, a favored motif for APOBEC3 enzymes. To systematically assess which of the mutations are functional and prioritize them for biological characterization, we developed Massively Parallel Reporter Assays (MPRA). Two types of MPRA libraries were constructed, one for evaluating promoter activity and the other for assessing enhancer activity of the mutations. Each library construction entailed 36,000 oligonucleotides, comprehensively representing all mutation hotspots and their corresponding wild-type alleles. The application of MPRA resulted in the identification of a substantial number of functionally relevant mutations. Zooming in on the top 20 genomic regions, we employed CRISPR-based approaches to pinpoint target genes and elucidate their functional consequences. Most of these regions co-localized with enhancers, as determined by analysis of ATAC-seq, ChIP-seq, and Hi-C data from bladder cancer cell lines. Perturbation of hotspots regions resulted in dysregulation of multiple genes. The co-regulation patterns of genes were further supported by strong correlation of their mRNA expression in RNA-seq datasets (TCGA, SRA, etc.). Notable examples included twin hotspot mutations upstream of the LEPROTL1 gene, elevating the expression of LEPROTL1, DCTN6 and SARAF, resulting in aggressive tumor growth in in vitro functional experiments. These three genes emerged as novel oncogenes in cancer. Another functional hotspot influenced the expression of two known long non-coding RNAs, NEAT1 and MALAT1, previously characterized as cancer genes. Additionally, some recurrently mutated regions or their target genes were associated with worse overall survival in the TCGA cohort. In conclusion, our findings propose that non-coding mutations likely drive tumorigenesis, contributing to tumor heterogeneity. Employing a combination of functional, multi-omics, and in vivo approaches, we aim to comprehensively characterize the role of hotspot non-coding mutations in bladder cancer and assess their clinical significance. Citation Format: Rouf Banday, Bilal Lone, Kelly Butler, Dhanusha Yesudhas, Alexandra Dobbins, Arup Chakraborty. Exploring the functional consequences of APOBEC3-induced non-coding hotspot mutations in bladder cancer using massively parallel reporter assays and CRISPR-mediated base editing [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr PR009.

Machine learning unveils an immune-related DNA methylation profile in germline DNA from breast cancer patients

BackgroundThere is an unmet need for precise biomarkers for early non-invasive breast cancer detection. Here, we aimed to identify blood-based DNA methylation biomarkers that are associated with breast cancer.MethodsDNA methylation profiling was performed for 524 Asian Chinese individuals, comprising 256 breast cancer patients and 268 age-matched healthy controls, using the Infinium MethylationEPIC array. Feature selection was applied to 649,688 CpG sites in the training set. Predictive models were built by training three machine learning models, with performance evaluated on an independent test set. Enrichment analysis to identify transcription factors binding to regions associated with the selected CpG sites and pathway analysis for genes located nearby were conducted.ResultsA methylation profile comprising 51 CpGs was identified that effectively distinguishes breast cancer patients from healthy controls achieving an AUC of 0.823 on an independent test set. Notably, it outperformed all four previously reported breast cancer-associated methylation profiles. Enrichment analysis revealed enrichment of genomic loci associated with the binding of immune modulating AP-1 transcription factors, while pathway analysis of nearby genes showed an overrepresentation of immune-related pathways.ConclusionThis study has identified a breast cancer-associated methylation profile that is immune-related to potential for early cancer detection.

Distinct sizes of maximal independent sets on graphs with restricted girth

Let $G$ be a graph. If $G$ has exactly $r$ distinct sizes of maximal independent sets, $G$ belongs to a collection called $\mathcal{M}_r$. If $G \in \mathcal{M}_{r}$ and the distinct values of its maximal independent sets are consecutive, then $G$ belongs to $\mathcal{I}_{r}$. The independence gap of $G$ is the difference between the maximum and the minimum sizes of a maximal independent set in $G$. In this paper, we show that recognizing graphs in $\mathcal{I}_r$ is $\mathcal{NP}$-complete, for every integer $r \geq 3$. On the other hand, we show that recognizing trees in $\mathcal{M}_r$ can be done in polynomial time, for every $r \geq 1$. Also, we present characterizations of some graphs with girth at least 6 with independence gap at least 1, including graphs with independence gap $r-1$, for $r\geq 2$, belonging to $\mathcal{I}_r$. Moreover, we present a characterization of some trees in $\mathcal{I}_3$.

Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity

BackgroundEarly prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) and investigate its value to improve pCR prediction as well as its association with tumor heterogeneity in breast cancer patients.MethodsThe DCE-MRI, clinicopathologic record, and full transcriptomic data of 785 breast cancer patients receiving neoadjuvant chemotherapy were retrospectively included from a public dataset. Dynamic features of DCE-MRI were computed from extracted phase-varying radiomic feature series using 22 CAnonical Time-sereis CHaracteristics. Dynamic model and radiomic model were developed by logistic regression using dynamic features and traditional radiomic features respectively. Various combined models with clinical factors were also developed to find the optimal combination and the significance of each components was evaluated. All the models were evaluated in independent test set in terms of area under receiver operating characteristic curve (AUC). To explore the potential underlying biological mechanisms, radiogenomic analysis was implemented on patient subgroups stratified by dynamic model to identify differentially expressed genes (DEGs) and enriched pathways.ResultsA 10-feature dynamic model and a 4-feature radiomic model were developed (AUC = 0.688, 95%CI: 0.635–0.741 and AUC = 0.650, 95%CI: 0.595–0.705) and tested (AUC = 0.686, 95%CI: 0.594–0.778 and AUC = 0.626, 95%CI: 0.529–0.722), with the dynamic model showing slightly higher AUC (train p = 0.181, test p = 0.222). The combined model of clinical, radiomic, and dynamic achieved the highest AUC in pCR prediction (train: 0.769, 95%CI: 0.722–0.816 and test: 0.762, 95%CI: 0.679–0.845). Compared with clinical-radiomic combined model (train AUC = 0.716, 95%CI: 0.665–0.767 and test AUC = 0.695, 95%CI: 0.656–0.714), adding the dynamic component brought significant improvement in model performance (train p < 0.001 and test p = 0.005). Radiogenomic analysis identified 297 DEGs, including CXCL9, CCL18, and HLA-DPB1 which are known to be associated with breast cancer prognosis or angiogenesis. Gene set enrichment analysis further revealed enrichment of gene ontology terms and pathways related to immune system.ConclusionDynamic characteristics of DCE-MRI were quantified and used to develop dynamic model for improving pCR prediction in breast cancer patients. The dynamic model was associated with tumor heterogeniety in prognostic-related gene expression and immune-related pathways.

ComorbidPGS: an R package assessing shared predisposition between Phenotypes using Polygenic Scores.

Introduction Polygenic Score (PGS) is a valuable method for assessing the estimated genetic liability to a given outcome or genetic variability contributing to a quantitative trait. While PRSs are widely used for complex traits, their application in uncovering shared genetic predisposition between phenotypes, i.e. when genetic variants influence more than one phenotype, remains limited. Methods We developed an R package, comorbidPGS, which facilitates a systematic evaluation of shared genetic effects among (cor)related phenotypes using PGSs. The comorbidPGS package takes as input a set of Single Nucleotide Polymorphisms (SNPs) along with their established effects on the original phenotype (Po), referred to as Po-PGS. It generates a comprehensive summary of effect(s) of Po-PGS on target phenotype(s) (Pt) with customisable graphical features. Results We applied comorbidPGS to investigate the shared genetic predisposition between phenotypes defining elevated blood pressure (Systolic Blood Pressure, SBP; Diastolic Blood Pressure, DBP; Pulse Pressure, PP) and several cancers (Breast Cancer, BrC; Pancreatic Cancer, PanC; Kidney Cancer, KidC; Prostate Cancer, PrC; Colorectal Cancer, CrC) using the European ancestry UK Biobank individuals and GWAS meta-analyses summary statistics from independent set of European ancestry individuals. We report a significant association between elevated DBP and the genetic risk of PrC (β (SE)=0.066 (0.017), P-value=9.64×10^(-5)), as well as between CrC PGS and both, lower SBP (β (SE)=-0.10 [0.029], P-value=3.83×10^(-4))) and lower DBP (β (SE)=-0.055 [0.017], P-value=1.05×10^(-3)). Our analysis highlights two nominally significant relationships for individuals with genetic predisposition to elevated SBP leading to higher risk of KidC (OR [95%CI]=1.04 [1.0039-1.087], P-value=2.82×10^(-2)) and PrC (OR [95%CI]=1.02 [1.003-1.041], P-value=2.22×10^(-2)). Conclusion Using comorbidPGS, we underscore mechanistic relationships between blood pressure regulation and susceptibility to three comorbid malignancies. This package offers valuable means to evaluate shared genetic susceptibility between (cor)related phenotypes through polygenic scores.

Some New Results on Domination and Independent Dominating Set of Some Graphs

One area of graph theory that has been studied in great detail is dominance in graphs. Applications for dominating sets are numerous. In wireless networking, dominant sets are used to find effective paths inside ad hoc mobile networks. They have also been used in the creation of document summaries and safe electrical grid systems. A set &amp;lt;I&amp;gt;S&amp;lt;/I&amp;gt;⊆&amp;lt;I&amp;gt;V&amp;lt;/I&amp;gt; is said to be dominating set of &amp;lt;I&amp;gt;G&amp;lt;/I&amp;gt; if for every &amp;lt;i&amp;gt;v &amp;lt;/i&amp;gt;є &amp;lt;I&amp;gt;V&amp;lt;/I&amp;gt;-&amp;lt;I&amp;gt;S&amp;lt;/I&amp;gt; there exists a vertex &amp;lt;i&amp;gt;u&amp;lt;/i&amp;gt; є &amp;lt;I&amp;gt;S&amp;lt;/I&amp;gt; such that &amp;lt;i&amp;gt;uv&amp;lt;/i&amp;gt; є &amp;lt;I&amp;gt;E&amp;lt;/I&amp;gt;. The dominance number of &amp;lt;I&amp;gt;G&amp;lt;/I&amp;gt;, represented by &amp;lt;i&amp;gt;γ&amp;lt;/i&amp;gt;(&amp;lt;I&amp;gt;G&amp;lt;/I&amp;gt;), is the lowest cardinality of vertices among the dominating set of &amp;lt;I&amp;gt;G&amp;lt;/I&amp;gt;. A classic NP-complete decision problem in computational complexity theory determines whether, given a graph &amp;lt;I&amp;gt;G&amp;lt;/I&amp;gt; and input &amp;lt;I&amp;gt;K&amp;lt;/I&amp;gt;, &amp;lt;i&amp;gt;γ&amp;lt;/i&amp;gt;(&amp;lt;I&amp;gt;G&amp;lt;/I&amp;gt;) ≤ &amp;lt;I&amp;gt;K&amp;lt;/I&amp;gt;. This is known as the dominating set issue. Consequently, it is thought that calculating &amp;lt;i&amp;gt;γ&amp;lt;/i&amp;gt;(&amp;lt;I&amp;gt;G&amp;lt;/I&amp;gt;) for each given graph &amp;lt;I&amp;gt;G&amp;lt;/I&amp;gt; may not be possible to do with a feasible algorithm. In addition to efficient approximation tactics, there exist efficient exact techniques for various graph classes. If there are no neighboring vertices in a subset &amp;lt;I&amp;gt;S&amp;lt;/I&amp;gt;, then &amp;lt;I&amp;gt;S&amp;lt;/I&amp;gt;⊆&amp;lt;I&amp;gt;V&amp;lt;/I&amp;gt; is an independent set. Additionally, the empty set and the subset with just one vertex are independent. An independent dominating set of &amp;lt;I&amp;gt;G&amp;lt;/I&amp;gt; is a set &amp;lt;I&amp;gt;S&amp;lt;/I&amp;gt; of vertices in a graph &amp;lt;I&amp;gt;G&amp;lt;/I&amp;gt; that is both an independent and a dominating set of &amp;lt;I&amp;gt;G&amp;lt;/I&amp;gt;. This paper&amp;apos;s primary goal is to investigate the dominance and independent dominating set of many graphs, including the line graph, the alternate triangular belt graph, the bistar graph, the triangular snake graph, and others.

A group target track‐before‐detect approach using two‐stage strategy with maximum‐likelihood probabilistic data association

AbstractIn tracking scenarios involving groups with dense targets, achieving effective data association is challenging due to mutual occlusion and interference among targets. The complexity of the tracking problem is further exacerbated in low‐observable environments by the increase in false alarm rates. The track‐before‐detect (TBD) is an advanced technology for detecting and tracking low‐observable targets, effectively mitigating data association problems by integrating multi‐frame echo data. However, the existing multi‐target TBD algorithms typically assume that the targets are spatially separated and are not suitable for scenarios involving group targets. A group target maximum‐likelihood probabilistic data association (GT‐ML‐PDA) algorithm, based on the concept of TBD, is proposed to track group targets effectively in low‐observable environments. The proposed algorithm divides group target tracking into two stages: group centre trajectory estimation and individual target trajectory estimation. To enhance the performance of the proposed algorithm, two strategies are suggested: modifying the equivalent measurements and extracting independent measurement sets for individual targets. Simulation results demonstrate that the proposed algorithm is capable of effectively tracking numerous individual targets within a group, even in the presence of heavy clutter.

Independence polynomials of graphs with given cover number or dominate number

For a graph [Formula: see text], let [Formula: see text] be the number of independent sets of size [Formula: see text] in [Formula: see text]. The independence polynomial [Formula: see text] has been the focus of considerable research. In this paper, using the coefficients of independence polynomials, we order graphs with some given parameters. We first determine the extremal graph whose all coefficients of [Formula: see text] are the largest (respectively, smallest) among all connected graphs (respectively, bipartite graphs) with given vertex cover number. Then we also derive the extremal graph whose all coefficients of [Formula: see text] are the largest among all connected graphs (respectively, bipartite graphs) with given vertex dominate number.

E-CLEAP: An ensemble learning model for efficient and accurate identification of antimicrobial peptides.

With the increasing problem of antimicrobial drug resistance, the search for new antimicrobial agents has become a crucial task in the field of medicine. Antimicrobial peptides, as a class of naturally occurring antimicrobial agents, possess broad-spectrum antimicrobial activity and lower risk of resistance development. However, traditional screening methods for antimicrobial peptides are inefficient, necessitating the development of an efficient screening model. In this study, we aimed to develop an ensemble learning model for the identification of antimicrobial peptides, named E-CLEAP, based on the Multilayer Perceptron Classifier (MLP Classifier). By considering multiple features, including amino acid composition (AAC) and pseudo amino acid composition (PseAAC) of antimicrobial peptides, we aimed to improve the accuracy and generalization ability of the identification process. To validate the superiority of our model, we employed five-fold cross-validation and compared it with other commonly used methods for antimicrobial peptide identification. In the experimental results on an independent test set, E-CLEAP achieved accuracies of 97.33% and 84% for the AAC and PseAAC features, respectively. The results demonstrated that our model outperformed other methods in all evaluation metrics. The findings of this study highlight the potential of the E-CLEAP model in enhancing the efficiency and accuracy of antimicrobial peptide screening, which holds significant implications for drug development, disease treatment, and biotechnology advancement. Future research can further optimize the model by incorporating additional features and information, as well as validating its reliability on larger datasets and in real-world environments. The source code and all datasets are publicly available at https://github.com/Wangsicheng52/E-CLEAP.

Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database.

In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression. In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n=10596, 70%) and a test set (n=4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long-short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model. In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897-0.916), 0.908 (95% CI 0.899-0.918), 0.910 (95% CI 0.901-0.919), and 0.793 (95% CI 0.769-0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival. Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients.

Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution of EPHX2: A Pilot Integrative Machine Learning Study

Suicide is a major public health problem caused by a complex interaction of various factors. Major depressive disorder (MDD) is the most prevalent psychiatric disorder associated with suicide; therefore, it is essential to prioritize suicide prediction and prevention within this population. Integrated information from different dimensions, including personality, cognitive function, and social and genetic factors, is necessary to improve the performance of predictive models. Besides, recent studies have indicated the critical roles for EPHX2/P2X2 in the pathophysiology of MDD. Our previous studies found an association of EPHX2 and P2X2 with suicide in MDD. This study is aimed at (1) establishing predictive models with integrated information to distinguish MDD from healthy volunteers, (2) estimating the suicide risk of MDD, and (3) determining the contribution of EPHX2/P2X2. This cross-sectional study was conducted on 472 prospectively collected participants. The machine learning (ML) technique using Extreme Gradient Boosting (XGBoost) classifier was employed to evaluate the performance and relative importance of the extracted characteristics in recognising patients with MDD and depressed suicide attempters (DSA). In independent validation set, the model with clinical and cognitive information could recognise MDD with an area under the receiver operating characteristic curve (AUC) of 0.938 (95% confidence interval (CI), 0.898–0.977), and genetic information did not improve classification performance. The model with clinical, cognitive, and genetic information resulted in a significantly higher AUC of 0.801 (95% CI, 0.719–0.884) for identifying DSA than the model with only clinical information, in which the three single nucleotide polymorphisms of EPHX2 showed important roles. This study successfully established step-by-step predictive ML models to estimate the risk of suicide attempts in MDD. We found that EPHX2 can help improve the performance of suicidal predictive models. This trial is registered with NCT05575713.

On conflict-free cuts: Algorithms and complexity

One way to define the Matching Cut problem is: Given a graph G, is there an edge-cut M of G such that M is an independent set in the line graph of G? We propose the more general Conflict-Free Cut problem: Together with the graph G, we are given a so-called conflict graph Gˆ on the edges of G, and we ask for an edge-cutset M of G that is independent in Gˆ. Since conflict-free settings are popular generalizations of classical optimization problems and Conflict-Free Cut was not considered in the literature so far, we start the study of the problem. We show that the problem is NP-complete even when the maximum degree of G is 5 and Gˆ is 1-regular. The same reduction implies an exponential lower bound on the solvability based on the Exponential Time Hypothesis. We also give parameterized complexity results: We show that the problem is fixed-parameter tractable with the vertex cover number of G as a parameter, and we show W[1]-hardness even when G has a feedback vertex set of size one, and the clique cover number of Gˆ is the parameter. Since the clique cover number of Gˆ is an upper bound on the independence number of Gˆ and thus the solution size, this implies W[1]-hardness when parameterized by the cut size. We list polynomial-time solvable cases and interesting open problems. At last, we draw a connection to a symmetric variant of SAT.

The ultraviolet continuum slopes of high-redshift galaxies: evidence for the emergence of dust-free stellar populations at z &amp;gt; 10

ABSTRACT We present an analysis of the ultraviolet (UV) continuum slopes (β) for a sample of 172 galaxy candidates at 8 &amp;lt; zphot &amp;lt; 16 selected from a combination of JWST NIRCam imaging and COSMOS/UltraVISTA ground-based near-infrared imaging. Focusing primarily on a new sample of 121 galaxies at 〈z〉 ≃ 11 selected from ≃ 320 arcmin2 of public JWST imaging data across 15 independent data sets, we investigate the evolution of β in the galaxy population at z ≥ 9. We find a significant trend between β and redshift, with the inverse-variance weighted mean UV slope evolving from 〈β〉 = −2.17 ± 0.06 at z = 9.5 to 〈β〉 = −2.59 ± 0.06 at z = 11.5. Based on a comparison with stellar population models including nebular continuum emission, we find that at z &amp;gt; 10.5 the average UV continuum slope is consistent with the intrinsic blue limit of dust-free stellar populations (βint ≃ −2.6). These results suggest that the moderately dust-reddened galaxy population at z &amp;lt; 10 was essentially unattenuated at z ≃ 11. The extremely blue galaxies being uncovered at z &amp;gt; 10 place important constraints on dust attenuation in galaxies in the early Universe, and imply that the already observed galaxy population is likely supplying an ionizing photon budget capable of maintaining ionized intergalactic medium fractions of ≳ 5 per cent at z ≃ 11.