Directly selecting cell-type marker genes for single-cell clustering analyses.

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8+ T cell types and potential prognostic marker genes.

The Two Fundamental Shapes of Sleep Heart Rate Dynamics and Their Connection to Mental Health in College Students.

Introduction: Wearable devices are rapidly improving our ability to observe health-related processes for extended durations in an unintrusive manner. In this study, we use wearable devices to understand how the shape of the heart rate curve during sleep relates to mental health. Methods: As part of the Lived Experiences Measured Using Rings Study (LEMURS), we collected heart rate measurements using the Oura ring (Gen3) for over 25,000 sleep periods and self-reported mental health indicators from roughly 600 first-year university students in the USA during the fall semester of 2022. Using clustering techniques, we find that the sleeping heart rate curves can be broadly separated into two categories that are mainly differentiated by how far along the sleep period the lowest heart rate is reached. Results: Sleep periods characterized by reaching the lowest heart rate later during sleep are also associated with shorter deep and REM sleep and longer light sleep, but not a difference in total sleep duration. Aggregating sleep periods at the individual level, we find that consistently reaching the lowest heart rate later during sleep is a significant predictor of (1) self-reported impairment due to anxiety or depression, (2) a prior mental health diagnosis, and (3) firsthand experience in traumatic events. This association is more pronounced among females. Conclusion: Our results show that the shape of the sleeping heart rate curve, which is only weakly correlated with descriptive statistics such as the average or the minimum heart rate, is a viable but mostly overlooked metric that can help quantify the relationship between sleep and mental health.

Damage evolution law of multi-hole blasting igneous rock and quantitative evaluation model of damage degree based on fractal theory and clustering algorithm.

The geological phenomenon of igneous rock invading coal seam is widely distributed, which induces mining risk and affects efficient mining. The pre-splitting blasting method of igneous rock is feasible but difficult to implement accurately, resulting in unnecessary safety and environmental pollution risks. In this paper, the blasting model with penetrating structural plane and the multi-hole blasting model with different hole spacing were established based on the Riedel-Hiermaier-Thoma (RHT) damage constitutive to explore the stress wave propagation law under detonation. The damage cloud diagram and damage degree algorithm were used to quantitatively describe the spatio-temporal evolution of blasting damage. The results show that the explosion stress wave presents a significant reflection stretching effect under the action of the structural plane, which can effectively aggravate the presplitting blasting degree of the rock mass inside the structural plane. The damage range of rock mass is synchronously evolved with the change of blasting hole spacing. The blasting in the igneous rock intrusion area of the 21,914 working face is taken as an application example, and the damage degree of rock mass is reasonably evaluated by the box-counting dimension and K-means clustering method, which proves the effectiveness of the blasting scheme and provides reference value for the implementation of related blasting projects.

Genetic Clustering of Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy Patients of Colorectal Origin: KRAS and Not TP53 Cluster Alterations are Associated with Poor Outcomes.

BACKGROUND: The prognostic impact of genetic mutations for patients who undergo cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) of colorectal origin (CRC) is not well defined. OBJECTIVE: We aimed to describe the genetic classifications in an unsupervised fashion, and the outcomes of this patient population. METHODS: A retrospective, bi-institutional study was performed on patients who underwent CRS-HIPEC with targeted mutation data with a median follow-up time of 61 months. Functional link analysis was performed using STRING v11.5. Genes with similar functional significance were clustered using unsupervised k-means clustering. Chi-square, Kaplan-Meier, and the log-rank test were used for comparative statistics. RESULTS: Sixty-four patients with peritoneal carcinomatosis from CRC origin underwent CRS-HIPEC between 2007 and 2022 and genetic mutation data were extracted. We identified 19 unique altered genes, with KRAS (56%), TP53 (33%), and APC (22%) being the most commonly altered; 12.5% had co-altered KRAS/TP53. After creating an interactome map, k-means clustering revealed three functional clusters. Reactome Pathway analysis on three clusters showed unique pathways (1): Ras/FGFR3 signaling; (2) p53 signaling; and (3): NOTCH signaling. Seventy-one percent of patients in cluster 1 had KRAS mutations and a median overall survival of 52.3 months (p < 0.05). CONCLUSIONS: Patients with peritoneal carcinomatosis (PC) of CRC origin who underwent CRS-HIPEC and with tumors that harbored mutations in cluster 1 (Ras/FGFR3 signaling) had worse outcomes. Pathway disruption and a cluster-centric perspective may affect prognosis more than individual genetic alterations in patients with PC of CRC origin.

Investigation of prognostic values of immune infiltration and LGMN expression in the microenvironment of osteosarcoma.

BACKGROUND: Osteosarcoma (OS), the most common primary malignant bone tumor, predominantly affects children and young adults and is characterized by high invasiveness and poor prognosis. Despite therapeutic advancements, the survival rate remains suboptimal, indicating an urgent need for novel biomarkers and therapeutic targets. This study aimed to investigate the prognostic significance of LGMN expression and immune cell infiltration in the tumor microenvironment of OS. METHODS: We performed an integrative bioinformatics analysis utilizing the GEO and TARGET-OS databases to identify differentially expressed genes (DEGs) associated with LGMN in OS. We conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to explore the biological pathways and functions. Additionally, we constructed protein-protein interaction (PPI) networks, a competing endogenous RNA (ceRNA) network, and applied the CIBERSORT algorithm to quantify immune cell infiltration. The diagnostic and prognostic values of LGMN were evaluated using the area under the receiver operating characteristic (ROC) curve and Cox regression analysis. Furthermore, we employed Consensus Clustering Analysis to explore the heterogeneity within OS samples based on LGMN expression. RESULTS: The analysis revealed significant upregulation of LGMN in OS tissues. DEGs were enriched in immune response and antigen processing pathways, suggesting LGMN's role in immune modulation within the TME. The PPI and ceRNA network analyses provided insights into the regulatory mechanisms involving LGMN. Immune cell infiltration analysis indicated a correlation between high LGMN expression and increased abundance of M2 macrophages, implicating an immunosuppressive role. The diagnostic AUC for LGMN was 0.799, demonstrating its potential as a diagnostic biomarker. High LGMN expression correlated with reduced overall survival (OS) and progression-free survival (PFS). Importantly, Consensus Clustering Analysis identified two distinct subtypes of OS, highlighting the heterogeneity and potential for personalized medicine approaches. CONCLUSIONS: Our study underscores the prognostic value of LGMN in osteosarcoma and its potential as a therapeutic target. The identification of LGMN-associated immune cell subsets and the discovery of distinct OS subtypes through Consensus Clustering Analysis provide new avenues for understanding the immunosuppressive TME of OS and may aid in the development of personalized treatment strategies. Further validation in larger cohorts is warranted to confirm these findings.

Proceedings of the EuBIC-MS developers meeting 2023.

The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.

Exploring the impacts of risk factors on mortality patterns of global Alzheimer's disease and related dementias from 1990 to 2021.

Alzheimer's Disease and Related Dementias (ADRD) affect millions of people worldwide, with mortality rates influenced by several risk factors and exhibiting significant heterogeneity across geographical regions. This study aimed to investigate the impact of risk factors on global ADRD mortality patterns from 1990 to 2021, utilizing clustering and modeling techniques. Data on ADRD mortality rates, cardiovascular disease, and diabetes prevalence were obtained for 204 countries from the GBD platform. Additional variables such as HDI, life expectancy, alcohol consumption, and tobacco use prevalence were sourced from the UNDP and WHO. All the data were extracted for men, women, and the overall population. Longitudinal k-means clustering and generalized estimating equations were applied for data analysis. The findings revealed that cardiovascular disease had significant positive effects of 1.84, 3.94, and 4.70 on men, women, and the overall ADRD mortality rates, respectively. Tobacco showed positive effects of 0.92, 0.13, and 0.39, while alcohol consumption had negative effects of - 0.59, - 9.92, and - 2.32, on men, women, and the overall ADRD mortality rates, respectively. The countries were classified into five distinct subgroups. Overall, cardiovascular disease and tobacco use were associated with increased ADRD mortality rates, while moderate alcohol consumption exhibited a protective effect. Notably, tobacco use showed a protective effect in cluster A, as did alcohol consumption in cluster B. The effects of risk factors on ADRD mortality rates varied among the clusters, highlighting the need for further investigation into the underlying causal factors.

Characterizing efficient feature selection for single-cell expression analysis.

Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.

Geotemporal analysis of melanoma incidence and mortality identifies distinct clusters of trends within the United States between 2001-2019.

The extent to which the geographic diversity of the U.S. plays a significant role in melanoma incidence and mortality over time has not been precisely characterized. We obtained age-adjusted melanoma data for the 50 states between the years 2001-2019 from the SEER registry and performed hierarchical clustering (complete linkage, Euclidean space) to uncover geotemporal trend groups over 2 decades. While there was a global increase in incidence during this time (b1=+0.41, p<0.0001), there were 6 distinct clusters (by absolute and Z-score) with significantly different temporal trends (ANCOVA p<0.0001). Cluster 2 (C2) states had the sharpest increase in incidence with b1=+0.66, p<0.0001. For mortality, the global rate decreased (b1=-0.03, p=.0003) with 3 and 6 clusters by absolute and Z scores, respectively (ANCOVA p<0.05). Cluster 1 (C1) states exhibited the smallest decline in mortality (b1=-0.017, p=0.008). Mortality to incidence ratios (MIRs) declined (b1=-0.0037, p<0.0001) and harbored 4 and 6 clusters by absolute and Z-score analysis, respectively (ANCOVA p<0.0001). Cluster 4 (C4) states had the lowest rate of MIR decline (b1=-0.003, p<0.0001). These results provide an unprecedented higher dimensional view of melanoma behavior over space and time. With more refined analyses, geospatial studies can uncover local trends which can inform public health agencies to more properly allocate resources.

GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer.

OBJECTIVE: Vital rules learned from FDG-PET radiomics of tumor subregional response can provide clinical decision support for precise treatment adaptation. We combined a rule-based machine learning (ML) model (RuleFit) with a heuristic algorithm (Gray Wolf Optimizer, GWO) for mid-chemoradiation FDG-PET response prediction in patients with locally advanced non-small cell lung cancer. Approach: Tumors subregions were identified using K-means clustering. GWO+RuleFit consists of three main parts: (i) a random forest is constructed based on conventional features or radiomic features extracted from tumor regions or subregions in FDG-PET images, from which the initial rules are generated; (ii) GWO is used for iterative rule selection; (iii) the selected rules are fit to a linear model to make predictions about the target variable. Two target variables were considered: a binary response measure (∆SUVmean⩾20% decline) for classification and a continuous response measure (∆SUVmean) for regression. GWO+RuleFit was benchmarked against common ML algorithms and RuleFit, with leave-one-out cross-validated performance evaluated by the area under the receiver operating characteristic curve (AUC) in classification and root-mean-square error (RMSE) in regression. Main results: GWO+RuleFit selected 15 rules from the radiomic feature dataset of 23 patients. For treatment response classification, GWO+RuleFit attained numerically better cross-validated performance than RuleFit across tumor regions and sets of features (AUC:0.58-0.86 vs. 0.52-0.78, p=0.170-0.925). GWO+Rulefit also had the best or second-best performance numerically compared to all other algorithms for all conditions. For treatment response regression prediction, GWO+RuleFit (RMSE:0.162-0.192) performed better numerically for low-dimensional models (p=0.097-0.614) and significantly better for high-dimensional models across all tumor regions except one (RMSE:0.189-0.219, p<0.004). Significance: The GWO+RuleFit selected rules were interpretable, highlighting distinct radiomic phenotypes that modulated treatment response. GWO+Rulefit achieved parsimonious models while maintaining utility for treatment response prediction, which can aid clinical decisions for patient risk stratification, treatment selection, and biologically driven adaptation. Clinical trial: NCT02773238.

A novel clustered-based detection method for shilling attack in private environments.

The topic of privacy-preserving collaborative filtering is gaining more and more attention. Nevertheless, privacy-preserving collaborative filtering techniques are vulnerable to shilling or profile injection assaults. Hence, it is crucial to identify counterfeit profiles in order to achieve total success. Various techniques have been devised to identify and prevent intrusion patterns from infiltrating the system. Nevertheless, these strategies are specifically designed for collaborative filtering algorithms that do not prioritize privacy. There is a scarcity of research on identifying shilling attacks in recommender systems that prioritize privacy. This work presents a novel technique for identifying shilling assaults in privacy-preserving collaborative filtering systems. We employ an ant colony clustering detection method to effectively identify and eliminate fake profiles that are created by six widely recognized shilling attacks on compromised data. The objective of the study is to categorize the fraudulent profiles into a specific cluster and separate this cluster from the system. Empirical experiments are conducted with actual data. The empirical findings demonstrate that the strategy derived from the study effectively eliminates fraudulent profiles in privacy-preserving collaborative filtering.

Association between triglyceride glucose-waist height ratio index and cardiovascular disease in middle-aged and older Chinese individuals: a nationwide cohort study.

BACKGROUND: The triglyceride-glucose (TyG) index and its combination with obesity indicators can predict cardiovascular diseases (CVD). However, there is limited research on the relationship between changes in the triglyceride glucose-waist height ratio (TyG-WHtR) and CVD. Our study aims to investigate the relationship between the change in the TyG-WHtR and the risk of CVD. METHODS: Participants were from the China Health and Retirement Longitudinal Study (CHARLS). CVD was defined as self-reporting heart disease and stroke. Participants were divided into three groups based on changes in TyG-WHtR using K-means cluster analysis. Multivariable binary logistic regression analysis was used to examine the association between different groups (based on the change of TyG-WHtR) and CVD. A restricted cubic spline (RCS) regression model was used to explore the potential nonlinear association of the cumulative TyG-WHtR and CVD events. RESULTS: During follow-up between 2015 and 2020, 623 (18.8%) of 3312 participants developed CVD. After adjusting for various potential confounders, compared to the participants with consistently low and stable TyG-WHtR, the risk of CVD was significantly higher in participants with moderate and increasing TyG-WHtR (OR 1.28, 95%CI 1.01-1.63) and participants with high TyG-WHtR with a slowly increasing trend (OR 1.58, 95%CI 1.16-2.15). Higher levels of cumulative TyG-WHtR were independently associated with a higher risk of CVD events (per SD, OR 1.27, 95%CI 1.12-1.43). CONCLUSIONS: For middle-aged and older adults, changes in the TyG-WHtR are independently associated with the risk of CVD. Maintaining a favorable TyG index, effective weight management, and a reasonable waist circumference contribute to preventing CVD.

The determinants of COVID-19 case reporting across Africa.

Background: According to study on the under-estimation of COVID-19 cases in African countries, the average daily case reporting rate was only 5.37% in the initial phase of the outbreak when there was little or no control measures. In this work, we aimed to identify the determinants of the case reporting and classify the African countries using the case reporting rates and the significant determinants. Methods: We used the COVID-19 daily case reporting rate estimated in the previous paper for 54 African countries as the response variable and 34 variables from demographics, socioeconomic, religion, education, and public health categories as the predictors. We adopted a generalized additive model with cubic spline for continuous predictors and linear relationship for categorical predictors to identify the significant covariates. In addition, we performed Hierarchical Clustering on Principal Components (HCPC) analysis on the reporting rates and significant continuous covariates of all countries. Results: 21 covariates were identified as significantly associated with COVID-19 case detection: total population, urban population, median age, life expectancy, GDP, democracy index, corruption, voice accountability, social media, internet filtering, air transport, human development index, literacy, Islam population, number of physicians, number of nurses, global health security, malaria incidence, diabetes incidence, lower respiratory and cardiovascular diseases prevalence. HCPC resulted in three major clusters for the 54 African countries: northern, southern and central essentially, with the northern having the best early case detection, followed by the southern and the central. Conclusion: Overall, northern and southern Africa had better early COVID-19 case identification compared to the central. There are a number of demographics, socioeconomic, public health factors that exhibited significant association with the early case detection.

Global current research status and future hotspots in osteoporotic fracture based on bibliometric assessment and visualization techniques.

BACKGROUND: In the past decade, the evolution of themes in the field of osteoporotic fractures has changed from epidemiology and prediction of long-term morbidity, risk assessment of osteoporotic fractures, and zoledronic acid and denosumab in the treatment of osteoporosis to treatment guidelines for osteoporosis and the side effects caused by anti-osteoporotic drugs. AIM: To understand the trends and hotspots in osteoporotic fracture research. METHODS: Original articles were retrieved between January 1, 2010, and December 31, 2019, from the Web of Science Core Collection database. CiteSpace software facilitated the analysis and visualization of scientific productivity and emerging trends. RESULTS: Nine studies were identified using bibliometric indices, including citation, centrality, and sigma value, which might indicate a growing trend. Through clustering, we identified six major hot subtopics. Using burst analysis, top-5 references with the strongest bursting strength after 2017 were identified, indicating a future hotspot in this field. CONCLUSION: Current hot subtopics in osteoporotic fracture research include atypical femoral fractures, androgen deprivation therapy, denosumab discontinuation, hip fractures, trabecular bone score (TBS), and bone phenotype. Management and prevention of secondary fractures in patients with osteoporotic fractures, TBSs, and long-term administration strategy for zoledronic acid are expected to become research hotspots.

Model-based multifacet clustering with high-dimensional omics applications.

High-dimensional omics data often contain intricate and multifaceted information, resulting in the coexistence of multiple plausible sample partitions based on different subsets of selected features. Conventional clustering methods typically yield only one clustering solution, limiting their capacity to fully capture all facets of cluster structures in high-dimensional data. To address this challenge, we propose a model-based multifacet clustering (MFClust) method based on a mixture of Gaussian mixture models, where the former mixture achieves facet assignment for gene features and the latter mixture determines cluster assignment of samples. We demonstrate superior facet and cluster assignment accuracy of MFClust through simulation studies. The proposed method is applied to three transcriptomic applications from postmortem brain and lung disease studies. The result captures multifacet clustering structures associated with critical clinical variables and provides intriguing biological insights for further hypothesis generation and discovery.

Development of a Novel Classification Approach for Cow Behavior Analysis Using Tracking Data and Unsupervised Machine Learning Techniques.

Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using human observations. We designed a novel framework using unsupervised learning techniques. The framework contains two steps. The first step segments cattle tracking data using state-of-the-art time series segmentation algorithms, and the second step groups segments into clusters and then labels the clusters. To evaluate the applicability of our proposed framework, we utilized GPS tracking data collected from five cows in a 1096 ha rangeland pasture. Cow movement pathways were grouped into six behavior clusters based on velocity (m/min) and distance from water. Again, using velocity, these six clusters were classified into walking, grazing, and resting behaviors. The mean velocity for predicted walking and grazing and resting behavior was 44, 13 and 2 min/min, respectively, which is similar to other research. Predicted diurnal behavior patterns showed two primary grazing bouts during early morning and evening, like in other studies. Our study demonstrates that the proposed two-step framework can use unlabeled GPS tracking data to predict cattle behavior without human observations.

Energy-Efficient, Cluster-Based Routing Protocol for Wireless Sensor Networks Using Fuzzy Logic and Quantum Annealing Algorithm.

The main limitation of wireless sensor networks (WSNs) lies in their reliance on battery power. Therefore, the primary focus of the current research is to determine how to transmit data in a rational and efficient way while simultaneously extending the network's lifespan. In this paper, a hybrid of a fuzzy logic system and a quantum annealing algorithm-based clustering and routing protocol (FQA) is proposed to improve the stability of the network and minimize energy consumption. The protocol uses a fuzzy inference system (FIS) to select appropriate cluster heads (CHs). In the routing phase, we used the quantum annealing algorithm to select the optimal route from the CHs and the base station (BS). Furthermore, we defined an energy threshold to filter candidate CHs in order to save computation time. Unlike with periodic clustering, we adopted an on-demand re-clustering mechanism to perform global maintenance of the network, thereby effectively reducing the computation and overhead. The FQA was compared with FRNSEER, BOA-ACO, OAFS-IMFO, and FC-RBAT in different scenarios from the perspective of energy consumption, alive nodes, network lifetime, and throughput. According to the simulation results, the FQA outperformed all the other methods in all scenarios.

A Machine Learning Approach to Predict Radiation Effects in Microelectronic Components.

This paper presents an innovative technique, Advanced Predictor of Electrical Parameters, based on machine learning methods to predict the degradation of electronic components under the effects of radiation. The term degradation refers to the way in which electrical parameters of the electronic components vary with the irradiation dose. This method consists of two sequential steps defined as 'recognition of degradation patterns in the database' and 'degradation prediction of new samples without any kind of irradiation'. The technique can be used under two different approaches called 'pure data driven' and 'model based'. In this paper, the use of Advanced Predictor of Electrical Parameters is shown for bipolar transistors, but the methodology is sufficiently general to be applied to any other component.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.