Biological Problems Research Articles

Spatially and temporally high-order dynamic nonlinear variational multiscale methods for generalized Newtonian flows

Non-Newtonian fluids are of interest in industrial sectors, biological problems and other natural phenomena. This work proposes rheologically-dependent, spatially and temporally high-order non-residual stabilized finite element formulations. The accuracy of the methods is assessed by tackling highly-convective time-dependent power-law flows. The spatial approximation uses Lagrangian finite elements up to fourth order. The temporal integration is done via backward differentiation formulas of order one, two and three. A key aspect of our work is using a non-residual orthogonal variational multiscale (VMS) formulation to stabilize dominant convection and to allow equal-order interpolation of velocity and pressure. Our VMS method uses dynamic nonlinear subscales, which have not been tested so far for generalized Newtonian fluids. In this work, the use of high-order temporal discretizations for the subscale components is systematically evaluated. Numerical experiments consider the flow over a confined cylinder for Reynolds numbers between 40 and 400 and power-law indices between 0.4 and 1.8. Numerical testing demonstrates the method to be stable in all combinations of spatial and temporal orders. Our results show that using high-order spatial discretizations more accurately approximates boundary layers and viscosity fields. Moreover, higher temporal orders allow using larger time steps while still capturing highly dynamic behaviors with better resolution in frequency spectra.

ScEMB: Learning context representation of genes based on large-scale single-cell transcriptomics.

The rapid advancement of single-cell transcriptomic technologies has led to the curation of millions of cellular profiles, providing unprecedented insights into cellular heterogeneity across various tissues and developmental stages. This growing wealth of data presents an opportunity to uncover complex gene-gene relationships, yet also poses significant computational challenges. We present scEMB, a transformer-based deep learning model developed to capture context-aware gene embeddings from large-scale single-cell transcriptomics data. Trained on over 30 million single-cell transcriptomes, scEMB utilizes an innovative binning strategy that integrates data across multiple platforms, effectively preserving both gene expression hierarchies and cell-type specificity. In downstream tasks such as batch integration, clustering, and cell type annotation, scEMB demonstrates superior performance compared to existing models like scGPT and Geneformer. Notably, scEMB excels in silico correlation analysis, accurately predicting gene perturbation effects in CRISPR-edited datasets and microglia state transition, identifying a few known Alzheimer's disease (AD) risks genes in top gene list. Additionally, scEMB offers robust fine-tuning capabilities for domain-specific applications, making it a versatile tool for tackling diverse biological problems such as therapeutic target discovery and disease modeling. scEMB represents a powerful tool for extracting biologically meaningful insights from complex gene expression data. Its ability to model in silico perturbation effects and conduct correlation analyses in the embedding space highlights its potential to accelerate discoveries in precision medicine and therapeutic development.

Immunomodulatory properties of splenocytes treated with chlorpromazine in experimental aggression

Aggressive behavior is considered to be as one of the central symptoms of many neuropsychiatric disorders. It is a serious medical and biological problem associated both with high frequency and severity of manifestations and lack of selective correction means. The purpose of this study was to evaluate the functional phenotype of immune cells treated with chlorpromazine in aggressive animals in vitro, as well as the effect of transplantation of these cells on syngeneic aggressive recipient’s immune cells functional activity. Aggressive behavior was formed in active mice as a result of repeated experience of victories in agonistic interactions with animals with a submissive partner. Further, aggressive animals were isolated into individual cells and used as donors and recipients of immune cells. Immune cells were obtained under sterile conditions from suspension of spleen cells precultured with chlorpromazine. The level of spontaneous and mitogen-induced cell proliferation was assessed using a standard method of radioactive label incorporation. The quantitative content of cytokines in samples of treated with chlorpromazine cell cultures supernatant was determined by enzyme immunoassay using appropriate test systems. Further aggressive syngeneic recipients were intravenously injected with splenocytes precultured with chlorpromazine. The control group of animals was injected with splenocytes precultured under similar conditions, without the chlorpromazine addition. The recipients were assessed for the spleen cells proliferative activity and the intensity of humoral and cellular immune response using standard methods, by the number of antibody-forming spleen cells and severity of a delayed-type hypersensitivity reaction in response to T-dependent antigen introduction. It was found that treatment with chlorpromazine in vitro suppressed mitogen-stimulated proliferation of splenocytes in aggressive mice, without changing of spontaneous proliferation. It was also accompanied by some cytokines production decrease: IL-6, IL-2 and IFNã. When studying the humoral and cellular immune response intensity in aggressive recipients after transplantation of donor’s syngeneic splenocytes treated with chlorpromazine, a decrease in the intensity of humoral immune response was recorded. Transplantation of donor’s splenocytes treated with chlorpromazine was also accompanied by a decrease in spontaneous and mitogen-stimulated proliferation of splenocytes from aggressive recipients. Thus, the obtained data indicate the inhibitory effect of chlorpromazine on immune cell’s functional activity in aggressive mice, as well as the positive immunomodulatory effect of chlorpromazine-treated immune cells transplantation at aggressive behavior in experimental animals.

Progress and opportunities of foundation models in bioinformatics.

Bioinformatics has undergone a paradigm shift in artificial intelligence (AI), particularly through foundation models (FMs), which address longstanding challenges in bioinformatics such as limited annotated data and data noise. These AI techniques have demonstrated remarkable efficacy across various downstream validation tasks, effectively representing diverse biological entities and heralding a new era in computational biology. The primary goal of this survey is to conduct a general investigation and summary of FMs in bioinformatics, tracing their evolutionary trajectory, current research landscape, and methodological frameworks. Our primary focus is on elucidating the application of FMs to specific biological problems, offering insights to guide the research community in choosing appropriate FMs for tasks like sequence analysis, structure prediction, and function annotation. Each section delves into the intricacies of the targeted challenges, contrasting the architectures and advancements of FMs with conventional methods and showcasing their utility across different biological domains. Further, this review scrutinizes the hurdles and constraints encountered by FMs in biology, including issues of data noise, model interpretability, and potential biases. This analysis provides a theoretical groundwork for understanding the circumstances under which certain FMs may exhibit suboptimal performance. Lastly, we outline prospective pathways and methodologies for the future development of FMs in biological research, facilitating ongoing innovation in the field. This comprehensive examination not only serves as an academic reference but also as a roadmap for forthcoming explorations and applications of FMs in biology.

Can the male germline offer insight into mammalian brain size expansion?

Recent advances in single-cell transcriptomic data have greatly expanded our understanding of both spermatogenesis and the molecular mechanisms of male infertility. However, this growing wealth of data could also shed light on a seemingly unrelated biological problem: the genetic basis of mammalian brain size expansion throughout evolution. It is now increasingly recognized that the testis and brain share many cellular and molecular similarities including pivotal roles for the RAS/MAPK and PI3K/AKT/mTOR pathways, mutations in which are known to have a pronounced impact on cell proliferation. Most notably, in the stem cell lineages of both organs, new mutations have been shown to increase cellular output over time. These include 'selfish' mutations in spermatogonial stem cells, which disproportionately increase the proportion of mutant sperm, and-to draw a parallel-human-specific mutations in neural stem cells which, by increasing the number of neurons, have been implicated in neocortical expansion. Here we speculate that the origin for many 'expansion'-associated mutations is the male germline and that as such, a deeper understanding of the mechanisms controlling testicular turnover may yield fresh insight into the biology and evolution of the brain.

Effect of Direct and Water-Mediated Interactions on the Identification of Hotspots in Biomolecular Complexes with Multiple Subsystems.

Identification of important residues in biochemical complexes is often a crucial step for many problems in molecular biology and biochemistry. A method is proposed to identify hotspots in biomolecular complexes based on a new metric, derived from networks representing molecular subunits (residues, bridging solvent molecules, ligands etc.) connected by interactions. A singular value decomposition of the weighted adjacency matrix is used to construct a scalar rank for each subunit that reflects its importance in the residue interaction network. This metric is called the singular value centrality. In addition, a new formalism is proposed to account for water-mediated interactions in the ranking of residues. Interactions for a residue network can be provided by various computational methods. In this work interactions are obtained from full quantum-mechanical calculations of protein-protein complexes using the fragment molecular orbital method. The ranking results are shown to be in good agreement with earlier computational and experimental studies. The developed method can be used to gain a deeper insight into the role of subunits in complex biomolecular systems.

From formulae, via models to theories: Dynamic Energy Budget theory illustrates requirements

As sciences mature, they transition from observation and description to explanation and prediction. This transition is associated with qualitative changes in the way quantitative mathematical formulations are constructed and interpreted, resulting in a ‘theory’. Such transitions from phenomenology to theory are happening in biology but the heuristic framework involved is rarely articulated. We here describe how the use of models in research sets model requirements, using Dynamic Energy Budget (DEB) theory to illustrate the more elaborate ones. We first make the distinction between mathematical formulae and models based on their relation to the abstract and real worlds. We then explain how the transition from models to theory affects model construction and parameter estimation, and discuss the concept of parameter estimation-in-context using the database Add_my_Pet on animal energetics. The transition comes with the need to develop auxiliary and meta- theory and to work with multiple datasets, implying constraints for the loss function that is used for parameter estimation. Finally, we discuss the extra requirements for general explanatory models: they need to be explicit on relevant general principles and to be embedded in a wider scientific context. We also discuss how we see theory’s relationship to observation and prediction change in the future as we use it to deal with theoretical and applied problems in biology.

Semi-supervised meta-learning elucidates understudied molecular interactions

Many biological problems are understudied due to experimental limitations and human biases. Although deep learning is promising in accelerating scientific discovery, its power compromises when applied to problems with scarcely labeled data and data distribution shifts. We develop a deep learning framework—Meta Model Agnostic Pseudo Label Learning (MMAPLE)—to address these challenges by effectively exploring out-of-distribution (OOD) unlabeled data when conventional transfer learning fails. The uniqueness of MMAPLE is to integrate the concept of meta-learning, transfer learning and semi-supervised learning into a unified framework. The power of MMAPLE is demonstrated in three applications in an OOD setting where chemicals or proteins in unseen data are dramatically different from those in training data: predicting drug-target interactions, hidden human metabolite-enzyme interactions, and understudied interspecies microbiome metabolite-human receptor interactions. MMAPLE achieves 11% to 242% improvement in the prediction-recall on multiple OOD benchmarks over various base models. Using MMAPLE, we reveal novel interspecies metabolite-protein interactions that are validated by activity assays and fill in missing links in microbiome-human interactions. MMAPLE is a general framework to explore previously unrecognized biological domains beyond the reach of present experimental and computational techniques.

ESTIMATION OF CANINE DIMENSIONS AS PREDICTORS OF SEX IN THE SOUTHERN BULGARIAN POPULATION

Purpose. Sex determination in forensic anthropology is an essential step for medicolegal purposes and very important for identification, as the number of possible matches will be halved. A dental profile includes all the individual dental traits and dimensions which can provide significant information on such human biological problems as the genetic relationships between populations and human environmental adaptation. The purpose of this research was to determine the sex differences in dental measurements of canines, inter-canine width, and canine indices in Bulgarians. Material and Methods. The study included 169 subjects of Bulgarian origin in the age group of 20 – 40 years. Buccolingual, mesio-distal and cervicoincisal diameters of canines and inter-canine width were measured by Dentistry Sliding Vernier Caliper and analyzed with SPSS 28.0. Maxillary and mandibular canine indices were calculated. Results. Mesio-distal and cervicoincisal dimensions of upper canines, inter-canine width and mandibular canine index exhibited statistically significant differences with a high degree of significance between males and females in contrast with the maxillary canine index of Bulgarian males and females, which revealed no statistically significant differences. Conclusion. In accordance with similar odontometric studies, canines, inter-canine width, and mandibular canine index may differentiate both sexes successfully, and thus they can be reliable predictors in forensic identification.

A Commentary. Another Unsolvable Problem and Axiom of Biology: It Is Impossible to Deduce Cell Properties in an Organism from the Properties of Its Constituent Cells

A Commentary. Another Unsolvable Problem and Axiom of Biology: It Is Impossible to Deduce Cell Properties in an Organism from the Properties of Its Constituent Cells

VEMcomp: a Virtual Elements MATLAB package for bulk-surface PDEs in 2D and 3D

AbstractWe present a Virtual Element MATLAB solver for elliptic and parabolic, linear and semilinear Partial Differential Equations (PDEs) in two and three space dimensions, which is coined VEMcomp. Such PDEs are widely applicable to describing problems in material sciences, engineering, cellular and developmental biology, among many other applications. The library covers linear and nonlinear models posed on different simple and complex geometries, involving time-dependent bulk, surface, and bulk-surface PDEs. The solver employs the Virtual Element Method (VEM) of lowest polynomial order $${k=1}$$ k = 1 on general polygonal and polyhedral meshes, including the Finite Element Method (FEM) of order $${k=1}$$ k = 1 as a special case when the considered mesh is simplicial. VEMcomp has three main purposes. First, VEMcomp generates polygonal and polyhedral meshes optimized for fast matrix assembly. Triangular and tetrahedral meshes are encompassed as special cases. For surface PDEs, VEMcomp is compatible with the well-known Matlab package DistMesh for mesh generation. Second, given a mesh for the considered geometry, possibly generated with an external package, VEMcomp computes all the matrices (mass and stiffness) required by the VEM or FEM method. Third, for multiple classes of stationary and time-dependent bulk, surface and bulk-surface PDEs, VEMcomp solves the considered PDE problem with the VEM or FEM in space and IMEX Euler in time, through a user-friendly interface. As an optional post-processing, VEMcomp comes with its own functions for plotting the numerical solutions and evaluating the error when possible. An extensive set of examples illustrates the usage of the library.

Affective disorders accompanied by cognitive impairment in patients with a cardiological profile: prevalence, multimorbidity, medical and social risk factors

Clinical and medico-social issues of affective pathology combined with cognitive impairment in cardiac patients are a serious problem of modern medicine. This multimorbidity is characterized by higher risks of social maladjustment, disability, concomitant health problems and mortality. Cognitive dysfunctions in affective pathology are of great clinical and social importance, but their initial manifestations in cardiac patients are still insufficiently diagnosed. This leads to missed opportunities for the prevention of cognitive deficits. This review presents epidemiological data, pathological mechanisms, biological and psychosocial problems of multimorbid connections of cardiovascular, affective and cognitive pathology. Full-text articles, fragments of monographs selected by keywords in the databases Scopus, Web of Science, Medline, RSCI, eLibrary.ru, disserCat.ru, Psychiatrist, Sciencedirect were used.

Simulation of the SIR dengue fever nonlinear model: A numerical approach

This paper introduces a new numerical technique that utilizes the Chebyshev collocation method to solve the nonlinear SIR mathematical model for dengue disease dynamics. The main goal of this study is the development of a highly accurate and efficient numerical approach that allows for the simulation of dengue fever infection and transmission mechanisms. The suggested methodology provides a strong and adaptable tool for studying the intricate dynamics of dengue fever, which is essential for improving our comprehension and control of this significant public health problem. The numerical approach is totally programmable and can be customized to incorporate a user-friendly interface for data input, hence enhancing its accessibility and practicality for researchers and public health practitioners. The conducted numerical simulations provide evidence of the accuracy and computational efficiency of the suggested technique. This unique numerical framework is positioned as a standard for managing and regulating a diverse array of biological problems. The creation of this sophisticated numerical tool signifies significant progress in the realm of dengue fever modeling. It offers a versatile and dependable platform to investigate different epidemiological scenarios and assess the possible effects of intervention efforts. The results of this study have significant consequences for enhancing dengue monitoring, safeguarding, and control initiatives in areas where the disease is prevalent.

WENDY: Covariance dynamics based gene regulatory network inference

Determining gene regulatory network (GRN) structure is a central problem in biology, with a variety of inference methods available for different types of data. For a widely prevalent and challenging use case, namely single-cell gene expression data measured after intervention at multiple time points with unknown joint distributions, there is only one known specifically developed method, which does not fully utilize the rich information contained in this data type. We develop an inference method for the GRN in this case, netWork infErence by covariaNce DYnamics, dubbed WENDY. The core idea of WENDY is to model the dynamics of the covariance matrix, and solve this dynamics as an optimization problem to determine the regulatory relationships. To evaluate its effectiveness, we compare WENDY with other inference methods using synthetic data and experimental data. Our results demonstrate that WENDY performs well across different data sets.

Пространственная сетка водородных связей в жидкостях и растворах

A review of the author's studies of the problem of a three-dimensional network of hydrogen bonds in liquids and solutions is presented. The main properties of the spatial network are revealed: stability, lability, elasticity and correlation of different types of motion of particles forming the network. Methods and results of description and study of three-dimensional networks are presented. The determining role of spatial networks of hydrogen bonds in a wide range of liquid problems of biology, physics and chemistry in applied and fundamental science is shown. It is proposed to distinguish liquids with a spatial network of hydrogen bonds into a separate class of solvents, headed by water.

TOMOMAN: a software package for large scale cryo-electron tomography data preprocessing, community data sharing, and collaborative computing.

Cryo-electron tomography (cryo-ET) and subtomogram averaging (STA) are becoming the preferred methodologies for investigating subcellular and macromolecular structures in native or near-native environments. While cryo-ET is amenable to a wide range of biological problems, these problems often have data processing requirements that need to be individually optimized, precluding the notion of a one-size-fits-all processing pipeline. Cryo-ET data processing is also becoming progressively more complex due to an increasing number of packages for each processing step. Though each package has its own strengths and weaknesses, independent development and different data formats makes them difficult to interface with one another. TOMOMAN (TOMOgram MANager) is an extensible package for streamlining the interoperability of packages, enabling users to develop project-specific processing workflows. TOMOMAN does this by maintaining an internal metadata format and wrapping external packages to manage and perform preprocessing, from raw tilt-series data to reconstructed tomograms. TOMOMAN can also export this metadata between various STA packages. TOMOMAN also includes tools for archiving projects to data repositories; allowing subsequent users to download TOMOMAN projects and directly resume processing where it was previously left off. By tracking essential metadata, TOMOMAN streamlines data sharing, which improves reproducibility of published results, reduces computational costs by minimizing reprocessing, and enables distributed cryo-ET projects between multiple groups and institutions. TOMOMAN provides a way for users to test different software packages to develop processing workflows that meet the specific needs of their biological questions and to distribute their results with the broader scientific community.

Cauchy universality and random billiards

Motion in bounded domains represents a paradigm in several settings: from billiard dynamics, to random walks in a finite lattice, with applications to relevant physical, ecological, and biological problems. A remarkable universal property, involving the average of return times to the boundary, has been theoretically proposed and experimentally verified in quite different contexts. We discuss here mechanisms that lead to violations of universality, induced by boundary effects and we also emphasize the role played by replacing straight lines with random walks in this framework. We suggest that our analysis should be relevant where nonhomogeneity appears in the stationary probability distribution in bounded domain. Published by the American Physical Society 2024

POSSIBILITIES OF USING METHODS AIMED AT SOLVING LOGICAL AND CRITICAL PROBLEMS IN BIOLOGY LESSONS IN GENERAL EDUCATION SCHOOLS

In the process of applying situational issues in teaching biology, the learning process is improved. Thus, it deepens interdisciplinary interaction and interdependence. Thus, in the process of applying situational problem solving technology, with the help of interdisciplinary integration, students create interaction between knowledge and can use it in the future. It is not enough to integrate the knowledge of one or two subjects in the teaching process, in this case, it is possible to form knowledge and skills related to different subjects as a result of solving situational issues related to the subject.

The Need for an Interactive Cell Biology Textbook Based on Case Studies by Integrating QR Codes

Cell Biology is a course whose material is abstract because it cannot be observed with the naked eye. The problem in learning Cell Biology is that no reference books can make abstract material more real to facilitate students' understanding. This research aims to analyze students' needs for Case Study-based interactive Cell Biology textbooks by integrating QR Codes. This research is quantitative and descriptive. The subjects in this research were Biology Education students. Data was obtained through interviews, observations, and questionnaires. Data were analyzed using quantitative descriptive methods. Based on data analysis, 67.6% of students stated that they could only imagine the structures and processes in cells, and 86.5% needed help understanding Cell Biology material. The results of the data analysis also show that all samples agreed to develop teaching materials that integrate QR codes connected to various videos or literature. Based on these results, an interactive cell biology textbook based on case studies needs to be created by further integrating QR codes.

Meta-analytical evidence for frequency-dependent selection across the tree of life.

Explaining the maintenance of genetic variation in fitness-related traits within populations is a fundamental challenge in ecology and evolutionary biology. Frequency-dependent selection (FDS) is one mechanism that can maintain such variation, especially when selection favours rare variants (negative FDS). However, our general knowledge about the occurrence of FDS, its strength and direction remain fragmented, limiting general inferences about this important evolutionary process. We systematically reviewed the published literature on FDS and assembled a database of 747 effect sizes from 101 studies to analyse the occurrence, strength, and direction of FDS, and the factors that could explain heterogeneity in FDS. Using a meta-analysis, we found that overall, FDS is more commonly negative, although not significantly when accounting for phylogeny. An analysis of absolute values of effect sizes, however, revealed the widespread occurrence of modest FDS. However, negative FDS was only significant in laboratory experiments and non-significant in mesocosms and field-based studies. Moreover, negative FDS was stronger in studies measuring fecundity and involving resource competition over studies using other fitness components or focused on other ecological interactions. Our study unveils key general patterns of FDS and points in future promising research directions that can help us understand a long-standing fundamental problem in evolutionary biology and its consequences for demography and ecological dynamics.