Abstract: Infectious diseases pose a significant global health burden, contributing to millions of deaths annually despite advancements in sanitation and healthcare access. This review systematically examines the role of machine learning in infectious disease prediction, diagnosis, and outbreak forecasting in the United States. We first categorize existing studies according to the type of disease and the ML methodology, highlighting key findings and emerging trends. We then examine the integration of hybrid and deep learning models, the application of natural language processing (NLP) in public health monitoring, and the use of generative models for medical image enhancement. In addition, we discuss the applications of machine learning in five diseases, including coronavirus disease 2019 (COVID-19), influenza (flu), human immunodeficiency virus (HIV), tuberculosis, and hepatitis, focusing on its role in diagnosis, outbreak prediction, and early detection. Our findings suggest that while machine learning has significantly improved disease detection and prediction, challenges remain in model generalizability, data quality, and interpretability.

Abstract: Music has a direct impact on human self-awareness through the semantic rules of language within a mathematical framework. A precise mathematical relationship exists between language, thought, imagination, consciousness, awareness, and self-awareness, rooted in the understanding of meaning and knowledge stored in memory. Music can enhance self-awareness, thereby directly influencing the neuroplasticity of the brain and shaping human personality. This study examines the formation process of self-awareness blocks in the brain and the mathematical connections between musical intervals, rhythm, and meaning. By analyzing the semantic aspects of numbers and language in the formation process of self-awareness and exploring the relationship between the meaning of numbers and the sensory reflection of musical intervals, the influence of music on human self-awareness is evaluated. The connection between various fields of self-awareness and the building blocks of these fields with musical motifs in phrases and sentences is among the findings of this research. This study elucidates how music can target and selectively affect the brain to assist in treating mental and physical illnesses, achieved through the precise articulation of musical phrases.

Abstract: We investigate the stochastic disruption of synchronization patterns in a system of two non-identical Rulkov neurons coupled via an electrical synapse. By analyzing the system deterministic dynamics, we identify regions of mono-, bi-, and tristability, corresponding to distinct synchronization regimes as a function of coupling strength. Introducing stochastic perturbations to the coupling parameter, we explore how noise influences synchronization patterns, leading to transitions between different regimes. Notably, we find that increasing noise intensity disrupts lag synchronization, resulting in intermittent switching between a synchronous 3-cycle regime and asynchronous chaotic states. This intermittency is closely linked to the structure of chaotic transient basins, and we determine a noise intensity range in which such behavior persists, depending on the coupling strength. Using both numerical simulations and an analytical confidence ellipse method, we provide a comprehensive characterization of these noise-induced effects. Our findings contribute to the understanding of stochastic synchronization phenomena in coupled neuronal systems and offer potential implications for neural dynamics in biological and artificial networks.

Abstract: As Virtual Reality technology is applied in medical domains deeply, surgical simulator is receiving widely attentions. As safe and efficient surgery training equip-ment, surgical simulator can provide surgeons with safe environment to practice sur-gical skills. The aim of surgical simulator is to calculate the responses of the soft tissue in surgery scenes caused by surgical tools. The basic task is to simulate deformation of soft tissue and cutting caused by scalpel. The non-linearity of the soft tissue and failure of pre-computed quantity caused by topology modification introduce great challenges for simulation of deformation and cutting within real-time. Dual-Mode Hybrid Dy-namic Finite Element Algorithm (DHD-FEA), the Finite Element Method (FEM) is adopted to simulate deformation of soft tissue. Nonlinear FEM model is applied to operational area for accuracy and linear FEM is applied to non-operational area for efficiency. A dynamic time integration scheme is applied to solve finite element equa-tions. Experiments show a good balance between accuracy and efficiency of defor-mation simulation.

Abstract: High-resolution temporal contact networks are useful ingredients for realistic epidemic simulations. Existing solutions typically rely either on empirical studies that capture fine-grained interactions via Bluetooth or wearable sensors in confined settings, or on large-scale simulation frameworks that model entire populations using generalized assumptions. However, for most realistic modeling of epidemic spread and the evaluation of countermeasures, there is a critical need for highly resolved, temporal contact networks that encompass multiple venues without sacrificing the intricate dynamics of real-world contacts. This paper presents an integrated approach for generating such networks by coupling Bayesian-optimized human mobility models (HuMMs) with a state-of-the-art epidemic simulation framework. Our primary contributions are twofold: First, we embed empirically calibrated HuMMs into an epidemic simulation environment to create a parameterizable, adaptive engine for producing synthetic, high-resolution, population-wide temporal contact network data. Second, we demonstrate through empirical evaluations that our generated networks exhibit realistic interaction structures and infection dynamics. In particular, our experiments reveal that while variations in population size do not affect the underlying network properties—a crucial feature for scalability—altering location capacities naturally influences local connectivity and epidemic outcomes. Additionally, sub-graph analyses confirm that different venue types display distinct network characteristics consistent with their real-world contact patterns. Overall, this integrated framework provides a scalable and empirically grounded method for epidemic simulation, offering a powerful tool for generating and simulating contact networks.

Abstract: The tumor microenvironment is a highly dynamic and complex system where cellular interactions evolve over time, influencing tumor growth, immune response, and treatment resistance. In this study, we develop a graph-theoretic framework to model the tumor microenvironment , where nodes represent different cell types, and edges denote their interactions. The temporal evolution of the tumor microenvironment is governed by fundamental biological processes, including proliferation, apoptosis, migration, and angiogenesis, which we model using differential equations with stochastic effects. Specifically, we describe tumor cell population dynamics using a logistic growth model incorporating both apoptosis and random fluctuations. Additionally, we construct a dynamic network to represent cellular interactions, allowing for an analysis of structural changes over time. Through numerical simulations, we investigate how key parameters such as proliferation rates, apoptosis thresholds, and stochastic fluctuations influence tumor progression and network topology. Our findings demonstrate that graph theory provides a powerful mathematical tool to analyze the spatiotemporal evolution of tumors, offering insights into potential therapeutic strategies. This approach has implications for optimizing cancer treatments by targeting critical network structures within the tumor microenvironment.

Abstract: Following Livadiotis G. and McComas D. J. (2023) [1], we propose a new type of DNA frameshift mutations that occur spontaneously due to information exchange between the DNA sequence of length bases (n) and the mutation sequence of length bases (m), and respect the kappa-addition symbol ⊕κ. We call these proposed mutations Kappa-Frameshift Background (KFB) mutations. We find entropy defects originate in the interdependence of the information length systems (or their interconnectedness, that is, the state in which systems with a significant number of constituents (information length bases) depend on, or are connected with each) by the proposed KFB-mutation). We also quantify the correlation among DNA information length bases (n) and (m) due to information exchange. In the presence of entropy defects, the Landauer’s bound and minimal metabolic rate for a biological system are modified. We observe that the different n and κ scales are manifested in the double evolutionary emergence of the proposed biological system through subsystems correlations. For specific values of the kappa parameter we can expect deterministic laws associated with a single biological polymer in the short term before the polymer explores over time all the possible ways it can exist.

Abstract: The purpose of the study is to describe possible behaviors of trajectories of a multi-dimensional system of ordinary differential equations that arise in the mathematical modeling of complex networks. This description is based on the combination of analytical and computational tools which allow to understand in general the behavior of trajectories. After the detailed treatment of the second-order case with multiple possible phase portraits the third order systems are considered. The emphasis is laid on the coexistence of several attracting sets. The role of knowing the attracting sets is discussed and explained. Further, the higher order systems are considered, of order four and higher. A way to obtain higher-order systems for a better understanding of them is provided. Due to the lack of results concerning modeling networks by systems of ordinary differential equations, special attention is paid to our previously obtained facts about the behavior of solutions of arbitrary order systems. The problem of control and management of such systems is discussed. Some suggestions are made.

Abstract:

Animal welfare research increasingly relies on behavioral analysis as a non-invasive and scalable alternative to traditional metabolic and hormonal indicators. However, there remains an annotation challenge due to the diversity and spontaneity of animal actions, which may require expertise and knowledge in annotations, thorough look-back examination, and re-annotation to ensure the models can generalize well. In this regard, a scheme to facilitate the annotation scenarios is to selectively annotate less proportional but informative samples, called "Active Learning." We comprehensively evaluated combining 7-Active learning and 11-Classifiers to expose their different converge effects until they are fine-tuned. Including 3-uncertainty, Random Sampling, Core-Set-Scores (CSS), Expected-Maximized-Change (EMC), and Density-Weighted Uncertainty (DWU) sampling strategies let classifiers of linear-based, boosting-based, rule-based, instance-based, backpropagation-based, and ensemble-based classifiers to simulate the annotation process on laying hens of 27-Classes dataset collected by sensors of accelerometer and gyroscope. Results indicate that simpler AL strategies in handled high-dimensional feature space outperform complex-designed AL in efficiency and performance. Also, we found that the ensemble classifiers (Random Forest Classifier and Extra Trees Classifier) and the boosting-based models (LightGBM and Hist Gradient Boosting Classifier) exhibited learning instabilities. Additionally, increasing the query batch sizes can enhance annotation efficiency with slight performance loss. These findings contribute to the advancement of efficient behavior recognition in precision livestock farming, offering a scalable framework while the real-world applications are appealing to well-annotated animal datasets.

Abstract: PITX2 (also known as pituitary homeobox 2), encodes a member of the RIEG/PITX homeobox family (a bicoid class of homeodomain proteins), that acts as a transcription factor and regulates procollagen lysyl hydroxylase gene expression. It is known to play an important role in pituitary, heart, brain, lungs, spleen, twisting of the gut and stomach, as well as the development of the eyes. Gujral and MacBeath [1] provides a quantitative, and dynamic study of WNT3A-mediated stimulation of HEK 293 cells, where they record time based expression profiles of several response genes which correlated significantly with proliferation and migration. By monitoring the dynamics of gene expression using self-organizing maps, they identified clusters of genes that exhibit similar expression dynamics and uncovered previously unrecognized positive and negative feedback loops. However, their study depicts/uses singular measurements of individual gene expression at different time snapshots/points to infer the system wide analysis of the pathway. At any particular time point, it is often the case that genes are working synergistically in combinations, even though their expression measurements are singular in nature. Here, I • enumerate and rank all 2415 PITX2 related 3rd order combinations in a forest of 71C3 combinations using four different sensitivity methods; • show the conserved rankings for PITX2-X-X combinations, which point to existence of biological synergy of some of these combinations across the different sensitivity methods; and • study the behaviour of some of these combinations related to WNT3A response genes that are ranked by the machine learning search engine (Sinha [2]) in time. Patterns of combinations emerge, some of which have been tested in wet lab, while others require further wet lab analysis.

Abstract: PORCN belongs to the family of membrane bound O-acyl transferase (MBOAT). MBOAT members contain multiple transmembrane domains and carry two conserved residues, a conserved histidine (His) embedded in a hydrophobic stretch of residues and an asparagine (Asn) or histidine within a more hydrophilic region some 30-50 residues upstream. It can add palmitoleate groups to WNT proteins that is necessary for WNT ligand secretion. Gujral and MacBeath [1] provides a quantitative, and dynamic study of WNT3A-mediated stimulation of HEK 293 cells, where they record time based expression profiles of several response genes which correlated significantly with proliferation and migration. By monitoring the dynamics of gene expression using self-organizing maps, they identified clusters of genes that exhibit similar expression dynamics and uncovered previously unrecognized positive and negative feedback loops. However, their study depicts/uses singular measurements of individual gene expression at different time snapshots/points to infer the system wide analysis of the pathway. At any particular time point, it is often the case that genes are working synergistically in combinations, even though their expression measurements are singular in nature. Here, I • enumerate and rank all 2415 PORCN related 3rd order combinations in a forest of 71C3 combinations using four different sensitivity methods; • show the conserved rank- ings for PORCN-X-X combinations, which point to existence of biological synergy of some of these combinations across the different sensitivity methods; and • study the behaviour of some of these combinations related to WNT3A response genes that are ranked by the machine learning search engine (Sinha [2]) in time. Patterns of combinations emerge, some of which have been tested in wet lab, while others require further wet lab analysis.

Abstract: The WNT proteins are a family of a number of cysteine-rich glycoproteins which activate signal transduction cascades via three different pathways, the canonical WNT pathway, the noncanonical planar cell polarity (PCP) pathway, and the noncanonical WNT/Ca2+ pathway. Here, a time behavioural study of 3rd order WNT combinations in WNT3A stimulated HEK 293 cells is presented. Gujral and MacBeath [1] provides a quantitative, and dynamic study of WNT3A-mediated stimulation of HEK 293 cells, where they record time based expression profiles of several response genes which correlated significantly with proliferation and migration. By monitoring the dynamics of gene expression using self-organizing maps, they identified clusters of genes that exhibit similar expression dynamics and uncovered previously unrecognized positive and negative feedback loops. However, their study depicts/uses singular measurements of individual gene expression at different time snapshots/points to infer the system wide analysis of the pathway. At any particular time point, it is often the case that genes are working synergistically in combinations, even though their expression measurements are singular in nature. Here, I • enumerate and rank all 2415 WNT (particular family member) related 3rd order combinations in a forest of 71C3 combinations using four different sensitivity methods; • show the conserved rankings for WNT-X-X combinations, which point to existence of biological synergy of some of these combinations across the different sensitivity methods; and • study the behaviour of some of these combinations related to WNT3A response genes that are ranked by the machine learning search engine (Sinha [2]) in time. Patterns of combinations emerge, some of which have been tested in wet lab, while others require further wet lab analysis.

Abstract: Persistent homology is a powerful tool in topological data analysis that captures the multi-scale topological features of data. In this work, we provide a mathematical introduction to persistent homology and demonstrate its application to protein-protein interaction networks. We combine persistent homology with algebraic connectivity, a graph-theoretic measure of network robustness, to analyze the topology and stability of PPI networks. An example is provided to illustrate the methodology and its potential applications in systems biology.

Abstract:

The implementation of artificial intelligence-based systems for disease detection using biomedical signals is challenging due to the limited availability of training data. The ability to synthetically augment training datasets is therefore crucial. This paper proposes using Long Short-Term Memory (LSTM) networks to learn long-term dependencies in non-linear time series, and subsequently employing the trained model to generate synthetic signals for improved training of detection systems. Linear models, such as AR, MA, or ARMA statistical models, are often inadequate due to the inherent non-linearity of the time series. The original data consist of Electroencephalogram (EEG) recordings.

Abstract: Gujral and MacBeath [1] provides a quantitative, and dynamic study of WNT3A-mediated stimulation of HEK 293 cells, where they record time based expression profiles of several response genes which correlated significantly with proliferation and migration. By monitoring the dynamics of gene expression using self-organizing maps, they identified clusters of genes that exhibit similar expression dynamics and uncovered previously unrecognized positive and negative feedback loops. However, their study depicts/uses singular measurements of individual gene expression at different time snapshots/points to infer the system wide analysis of the WNT pathway. At any particular time point, it is often the case that genes are working synergistically in combinations, even though their expression measurements are singular in nature. Sinha [2] recently demonstrated the use of machine learning based search engine to rank/reveal gene combinations at 2nd order for the time series data by Gujral and MacBeath [1] and showed how it is possible to locate combinations of priority that might be working synergistically. However, the problem explodes combinatorially with even a small set of 71 recorded genes in the above study, when one steps to explore 3rd order combinations. With the total number of 71C3 (= 57155) combinations, it becomes nearly impossible for any biologist to study the system wide dynamics of any pathway. Here, I • enumerate and rank all 71C3 combinations using four different sensitivity methods; • show the conserved rankings for PORCN-WNT-X combinations, which point to existence of biological synergy of some of these combinations across the different sensitivity methods; and • study the behaviour of some of the combinations related to WNT3A response genes that are ranked by the search engine in time. This study demonstrates how biologists can use the machine learning based search engine to address the needle in a haystack problem of discovering meaningful combinations of higher order in a vast search forest, which on further wet lab test might assist in intervening the pathway at a combinatorial level, in time.

Abstract: This work presents a mechanistic nonlinear model to explore the dynamics of Chronic Lymphocytic Leukemia (CLL) under combined chemoimmunotherapy with CAR-T cells and chlorambucil. The model, formulated by a set of three first-order Ordinary Differential Equations (ODEs), captures the short- and long-term effects of both therapies on the leukemia cells population. Utilizing a model-based design approach, we determine optimal dosing strategies through extensive in silico experimentation. Nonlinear system theories are applied to analyze the local and global dynamics of the system, allowing us to derive sufficient conditions for therapy dosing and treatment protocol design. Additionally, we compare dose-escalation and dose de-escalation protocols, illustrating the potential for complete eradication, partial response or relapse if therapies fail to completely eradicate cancer. Our results emphasize the utility of mathematical modeling in improving treatment strategies on the emerging field of personalized medicine.

Abstract: During the past decades, delayed differential dynamical equation has displayed a great application in portraying the interplay of diverse chemical substances in chemistry. In the research, relying the earlier publications, we propose a novel nutrient-microorganism system accompanying time delay. Exploiting fixed point theorem, inequality skills and a proper function, we acquire the parameter criteria on well-posedness (for example, existence and uniqueness, non-negativeness and boundedness) of the solution of the established delayed nutrient-microorganism system. Utilizing Hopf bifurcation theory and stability criterion of delayed dynamical system, we deal with the onset of Hopf bifurcation phenomenon and stability trait of the established delayed nutrient-microorganism system. A set of novel delay-independent parameter conditions on bifurcation phenomenon and stability of the system are provided. Designing both different hybrid delayed feedback controllers, we have successfully controlled the time of occurrence of bifurcation phenomenon and stability domain of the system. The role of time delay in controlling bifurcation and stabilizing system is analyzed. Numerical experiment figures are given to support the gained primary outcomes. The acquired assertions of this research are completely innovative and can give some important instructions in regulating and balancing the densities of microorganism and nutrient.

Abstract: A recent design of a machine learning based search engine was published, that ranks combinations of genes that might be working synergistically in cells in various processes. To demonstrate its efficacy in real life scenario, the data set containing recordings of up/down regulated genes generated from colorectal cancer (CRC) cells treated with PROCN-WNT inhibitor drug ETC-1922159 was taken. The regulation of the genes were recorded individually, but in many cases, it is still not known which higher (≥ 2) order gene combinations might be playing a greater role in CRC. Here, I demonstrate that the rankings assigned to gene combinations at 2nd order, by the search engine are conserved across the different sensitivity methods (and kernels/variants). This conservation points to the possible existence of the synergy between genes at the biological level. To establish the hypothesis I present ranked combinations of v-myc avian myelocytomatosis viral oncogene homolog (MYC), known to encode proteins that play significant role as transcription factors in cancer and target various kinds of genes, thus contributing to regrowth and proliferation. The manuscript identifies experimentally tested combinations of MYC-X in literature (whether in CRC cell or other cancer/ordinary cell). Second, the work reveals machine learning rankings for these MYC-X combinations in ETC-1922159 treated CRC cells. For experimentally established combinations, these rankings bolster confirmatory results. Based on the second step, the work points to new rankings of unknown/untested/unexplored MYC-X com- binations that might be working synergistically in CRC cells.

Abstract: In response to DNA damage and replication stress, RHINO RAD9-HUS1-RAD1 interacting nuclear orphan (RHNO1), interacts with RAD9-HUS1-RAD1 (9-1-1) clamp and TOPBP1, to activate ATR signaling pathway. Recently, it has been found to be implicated in cancer as it is often overexpressed. In colorectal cancer (CRC) cells treated with ETC-1922159, RHNO1 was found to be down regulated along with other genes. A recently developed search engine ranked combinations of RHNO1-X (X, a particular gene/protein) at 2nd order level after drug administration. Some of these combinations have been tested in wet lab, however many have been pointed out by the search engine that are yet to be explored/tested. These rankings reveal which RHNO1-X combina- tions might be working synergistically in CRC. In this research work, I cover combinations of RHNO1 with possible members of DNA topoisomerase (TOP), nei like DNA glycosylase (NEIL), flap structure-specific endonuclease 1 (FEN1), tumor protein p53 (TP53), ATR serine/threonine kinase (ATR), cell division cycle (CDC), forkhead box (FOX) and bone morphogenetic protein (BMP) family.

Abstract: Fanconi anemia complementation group D2 (FANCD2) is one of the members of FA complementation group, that is activated in response to DNA damage and is involved in DNA repair and regulation of genomic stability. FANCD2 works with other members of FANC group along with other proteins/genes, to carry out its required functionality. Further, disruption of FA/BRCA pathway has been implicated in cancer progression. In colorectal cancer (CRC) cells treated with ETC-1922159, FANCD2 was found to be down regulated along with other genes. A recently developed search engine ranked combinations of FANCD2-X (X, a particular gene/protein) at 2nd order level after drug administration. Some of these combinations have been tested and established in wet lab, however many have been pointed out by the search engine that are yet to be explored/tested. These rankings reveal which FANCD2-X combinations might be working synergistically in CRC. In this research work, I cover combinations of FANCD2 with, REV3 like DNA directed polymerase zeta catalytic subunit (REV3L), Bloom syndrome RecQ like helicase (BLM), MRE11 homolog double strand break repair nuclease (MRE11A), Wnt family member 10B (WNT10B), ubiquitin like with PHD and ring finger domains 1 (UHRF1), hes family bHLH transcription factor 1 (HES1), BRCA DNA repair associated (BRCA), RAD51 recombinase (RAD51), ERCC excision repair endonuclease non-catalytic subunit (ERCC), KIAA, X-ray repair cross com- plementing (XRCC), structural maintenance of chromosomes (SMC), WD repeat do- main containing (WDR), ubiquitin conjugating enzyme E2 (UBE2), cell division cycle (CDC), importin (IPO), aldehyde dehydrogenase family member (ALDH), H2A variant histone (H2A), heat shock protein (HSP), cyclin dependent kinase (CDK), dynein axonemal heavy chain (DNAH), forkhead box (FOX), ring finger protein (RNF), E2F transcription factor (E2F) and small nucleolar RNA host gene (SNHG) family.