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Multiple Mean Comparison for Clusters of Gene Expression Data through the t-SNE Plot and PCA Dimension Reduction
Full text linkThis paper introduces a novel methodology for multiple mean comparison of clusters identified in gene expression data through the t-distributed Stochastic Neighbor Embedding (t-SNE) plot, which is a powerful dimensionality re- duction technique for visualizing high-dimensional gene expression data. Our approach integrates the t-SNE visualization with rigorous statistical testing to validate the differences between identified clusters, bridging the gap between exploratory and confirmatory data analysis. We applied our methodology to two real-world gene expression datasets for which the t-SNE plots provided clear separation of clusters corresponding to different expression levels. Our findings underscore the value of combining the t-SNE visualization with multiple mean comparison in gene expression analysis. This integrated approach enhances the interpretability of complex data and provides a robust statistical framework for validating observed patterns. While the classical MANOVA method can be applied to the same multiple mean comparison, it requires a larger total sample size than the data dimension and mostly relies on an asymptotic null distribution. The proposed approach in this paper has broad applicability in the case of high dimension with small sample sizes and an exact null distribution of the test statistic.
Objective: Propose a two-step approach to analysis of gene expression data.
Gene expression data usually possess a complicated nonlinear structure that cannot be visualized under simple linear dimension reduction like the principal component analysis (PCA) method. We propose to employ the existing t-SNE approach to dimension reduction first so that clusters among data can be clearly visualized and then multiple mean comparison methods can be further employed to carry out statistical inference. We propose the PCA-type projected exact F-test for multiple mean comparison among the clusters. It is superior to the classical MANOVA method in the case of high dimension and relatively large number of clusters.
Results: Based on a simple Monte Carlo study on a comparison between the projected F-test and the classical MANOVA Wilks’ Lambda-test and an illustration of two real datasets, we show that the projected F-test has better empirical power performance than the classical Wilks’ Lambda-test. After applying the t-SNE plot to real gene expression data, one can visualize the clear cluster structure. The projected F-test further enhances the interpretability of the t-SNE plot, validating the significant differences among the visualized clusters.
Conclusion: Our findings suggest that the combination of the t-SNE visualization and multiple mean comparison through the PCA-projected exact F-test is a valuable tool for gene expression analysis. It not only enhances the interpretability of high-dimensional data but also provides a rigorous statistical framework for validating the observed patterns
Transforming Breast Cancer Prediction: Advanced Machine Learning Models for Accurate Prediction and Personalized Care
Full text linkBackground: Breast cancer is the most common malignancy among women worldwide, underscoring the importance of early detection and accurate prognostication. Machine learning (ML) has emerged as a promising approach, offering powerful tools for analyzing complex datasets in breast cancer prediction and diagnosis.
Objective: This study evaluates the predictive performance of diverse ML algorithms for breast cancer classification using publicly available datasets, focusing on accuracy, interpretability, and generalizability.
Methods: The dataset included clinical and demographic variables such as age, menopausal status, tumor size, and lymph node involvement. Data preprocessing addressed missing values and class imbalance, with the Synthetic Minority Oversampling Technique (SMOTE) applied to improve sensitivity for the minority class. Feature engineering involved interaction terms and scaling of numerical variables. Multiple ML models—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), and Neural Networks—were trained and evaluated. Performance was measured using sensitivity, F1-score, and AUC-ROC. Model interpretability was enhanced with SHapley Additive exPlanations (SHAP).
Results: Random Forest achieved the best performance with an AUC-ROC of 0.9751, followed by Gradient Boosting (0.9242) and Neural Networks (0.9254). Logistic Regression and SVM yielded comparable results (0.9005 and 0.9344). Ensemble models showed higher accuracy and generalizability, particularly on external validation. Tumor size and lymph node involvement emerged as key predictors. SMOTE improved sensitivity across models.
Conclusion: This study demonstrates the potential of ML in breast cancer prediction, emphasizing the effectiveness of ensemble methods and interpretability tools. Future work should focus on integrating ML into clinical practice for earlier detection and personalized treatment
Intelligent MRI Analysis for Parkinson’s Disease Detection
Full text linkThis study presents a practical approach for classifying Magnetic Resonance Imaging (MRI) scans to distinguish between normal subjects and those affected by Parkinson’s disease (PD). PD is a progressive brain disorder marked by dopamine deficiency, and lacks reliable diagnostic methods for early detection. To overcome this challenge, we employed Scale-Invariant Feature Transform (SIFT) and Local Binary Pattern (LBP) in designing a Computer-Aided Diagnostic (CAD) System. The extracted features are classified using K-Nearest Neighbour (KNN) and Decision Tree algorithms. Experimental results show that LBP features classified through the Decision Tree achieved the highest accuracy of 97.41%, demonstrating the efficiency of the proposed method in achieving early and accurate detection of PD
Comparative Analysis of Parametric Survival Models in HIV Patient Data
Full text linkThis study explores the efficacy of four key parametric survival models-Weibull, Gompertz, Lomax, and Exponential-in assessing mortality risk among HIV-positive patients undergoing antiretroviral therapy (ART). The research examined a retrospective cohort of 2,794 individuals, noting 124 deaths (4.4%) and 2,670 censored cases (95.6%), utilizing time-to-event data. Each model was estimated using maximum likelihood estimation (MLE) and assessed using various model selection criteria, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The Gompertz distribution emerged as the best fit (AIC = 45,943.33; BIC = 45,961.58), followed by the Weibull model, while the Lomax and Exponential models showed higher AIC/BIC values and less stable fits. The optimized parameters for the Gompertz model were determined as l = 0.00316 and a = 1.77x10-6, indicating a gradually increasing hazard rate over time. Model adequacy was further confirmed using Cox-Snell residuals (via Nelson-Aalen cumulative hazard) and Cox-Snell residual Q-Q plots for diagnostic evaluation. The Gompertz model demonstrated the highest coefficient of determination (R2 = 0.9817), followed by the Weibull (R2 = 0.9168), while the Lomax and Exponential models both had lower R2 values (0.5989), underscoring the superior predictive capability of the Gompertz model. Additionally, Cox proportional hazards regression identified significant mortality predictors, such as age at ART initiation (HR = 1.05, p < 0.001), male sex (HR = 1.60, p < 0.01), and last recorded body weight (HR = 0.94, p < 0.001). In contrast, baseline CD4 count and WHO stage were not significant. The model’s concordance index (C = 0.85) indicated high predictive accuracy. This study is motivated by the ongoing variability in HIV survival outcomes despite the extensive use of ART. By comparing these parametric models, the research enhances the understanding of mortality dynamics, aiding clinicians and policymakers in selecting optimal model structures for precise survival prediction, improved ART program monitoring, and informed patient management.These findings highlight significant clinical implications for HIV care, identifying age at ART initiation, male sex, and lower body weight as mortality predictors,indicating where targeted actions are needed. The Gompertz model’s superior performance offers a robust method for the prediction of long-term survival, underlining the need for monitoring comorbidities and the management of treatment-related side effects. With this model, HIV programs will be better positioned to flag high-risk patients, time interventions more appropriately, and allocate resources to reduce preventable deaths among their aging populations
Effect of MAX Phase Mo2TiAlC2 on the PVDF Ultrafiltration Membrane Properties and Performance
Full text linkUltrafiltration membranes are widely used in wastewater filtration due to their efficiency relative to conventional water treatment technologies. To improve the antifouling property of the PVDF membrane, a composite ultrafiltration membrane was fabricated employing the in-situ embedment approach throughout the phase inversion process and utilizing a new 2D material, MAX phase Molybdenum Titanium Aluminium Carbide (Mo2TiAlC2). The membranes were described using Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), and porosity measurements. Rejection tests were applied to study the produced membranes. Adding Mo2TiAlC2 increased the hydrophilicity of the composite membrane compared to the pristine membrane. Porosity and membrane pore size increased with the addition up to 0.6% wt. The most hydrophilic membrane (M3) recorded the highest protein rejection of 84.9%, which was much higher than that of the pristine membrane. These findings highlight the potential of Mo2TiAlC2 as a promising PVDF membrane additive
Asymmetry of the Developing Brain, Structural Anomalies, and Genetic Variants in the Pathogenesis of Unilateral Spastic Cerebral Palsy (uCP), a Common Neurological Symptom in Intellectual Disability, is Discussed in the form of a Narrative Overview
No full textAsymmetrical form and structural features of the brain can occur both as physiological hemispheric differences and as pathological left-right disparities. This review starts with findings on physiological brain development. It focuses primarily on non-physiological asymmetries between the left and right brain hemispheres and their impact on brain function, particularly motor functions. These are discussed in the context of cerebral palsy, specifically unilateral cerebral palsy, with a particular emphasis on genetic aspects. Pathogenic variants in specific genes can have diverse effects on structural brain development and, consequently, brain function. Several groups of genes must be distinguished based on their impact on the developing brain. These include variants in genes related to the coagulation system, angiogenesis, mitochondrial functions, and oxidative phosphorylation, which contribute to encephaloclastic lesions in the developing brain (e.g., periventricular or subcortical leukomalacia). These are distinct from gene variants that lead to disruptions in neuronal induction, proliferation, migration, aggregation, differentiation, and synaptic connectivity. Neurological symptoms, such as the development of spastic hemiparesis/cerebral palsy, can arise from genetically caused structural-functional disorders at both macroscopic (e.g., hemimegalencephaly) and microscopic levels (e.g., synaptic scaffolding). Additionally, disruptions in the structure and function of perineuronal networks must also be considered in this context.
The ultimate goal of this review is to describe and discuss the pathways involved in the pathogenesis of unilateral cerebral palsy in a differentiated manner, with a particular focus on molecular genetic aspects
From 1997 to 2025: The Evolving Relevance of 'Ma 6-T Va Crack-Er' in Modern France
Full text linkThis study investigates the continued relevance of Jean-François Richet's Ma 6-T va crack-er in addressing France's persistent socio-political issues related to the banlieues. Combining historical analysis, theoretical frameworks, and comparative film studies, the paper explores how themes of urban segregation, cultural identity, systemic inequality, and youth rebellion remain pressing in 2025. It also examines how successive policy failures and media representations have entrenched negative perceptions of these marginalised communities. Drawing on the works of Hall, Balibar, Wacquant, Gilroy, and Crenshaw, this paper offers a multidisciplinary analysis while proposing policy solutions to address the long-standing issues in the banlieues
Well-Being: The Keystone of Sustainable Social Development and Social Entrepreneurism
Full text linkIn an interconnected world, global challenges like climate change transcend borders, complicating governance as national interests often clash. Unregulated information flows fuel misinformation and erode trust in institutions. Today’s governance resembles a complex puzzle, where addressing one issue may exacerbate another. The rise of e-commerce has transformed consumer purchasing behaviors and research, yet marketing lags in adapting to this shift, focusing on digital media rather than engaging consumers.
This paper investigates how societal narratives and digital engagement can guide governance and marketing in a complex, interconnected world where trust in traditional institutions is declining. It introduces the Virtual Living Lab (VLL), a tool that analyzes social media, Big Data, and AI to track emerging public priorities and behavioral patterns. Research conducted in Japan and the UK during the COVID-19 pandemic reveals that well-being narratives play a vital role in shaping societal recovery and resilience. The findings emphasize that effective engagement requires not only relevant content but also emotional and contextual awareness, especially in times of uncertainty. As public perceptions of Well-Being evolve, particularly in Japan, organizations must ensure their messaging remains emotionally resonant and contextually relevant to foster behavioral change and promote healthier, more sustainable lifestyles
The Media’s Impact on Democratisation and Conflicts in Africa: An Analysis of Recent Trends
Full text linkThe fall of the Berlin Wall in 1989, and the end of the Cold War, did not have an effect only on the Germans whose countries were re-united, or the combatants of the Cold War – the United States and the Soviet Union. Rather, the effects of these events affected many countries worldwide, especially developing countries who have since witnessed an unprecedented political and democratic reawakening.
As the wave of democratisation continues to move across Africa, conflicts have almost become a by-product, where people, divided on ethnicity and/or religion, fight to establish their presence and dominance in government. One institution associated with the democratisation process and conflicts in Africa is the media. This article reviews some of the democratic processes that have taken place in Africa and the associated conflicts and the role the media have played in both. The article thus contributes to the literature on both democracy in Africa and its associated conflicts and the media’s role. It concludes that, whereas the media played key roles in some conflicts, they have played crucial roles in the promotion of democracy in Africa
Children Allergies in Saudi Arabia: The Situation and Challenges‒ Narrative Review
Full text linkPediatric allergic diseases like asthma, allergic rhinitis, eczema, and food allergies are highly prevalent in Saudi Arabia. This narrative review synthesizes diverse evidence on the prevalence, clinical presentation, and management of pediatric allergic diseases in Saudi Arabia, highlighting gaps in knowledge and practice to inform future healthcare strategies. Studies report allergy rates between 13-45% among Saudi children. Common medications used include antihistamines, nasal steroids, bronchodilators, and topical creams. While specific immunotherapy is growing in popularity for persistent IgE-mediated conditions. This paper reviews recent literature on the burden, management strategies, treatment options, and challenges regarding pediatric allergies in Saudi Arabia. Key challenges include a lack of awareness among families and even healthcare professionals, a shortage of pediatric allergy specialists, limited accessibility, high treatment costs, and a lack of standardized protocols. However, advancements in immunotherapy and oral food desensitization provide promising prospects. More research, public health initiatives, specialized workforce capacity building, improving affordability, and national guidelines will help address this major pediatric concern in the kingdom