Biologists Research Articles

Universal high-throughput image quantification of subcellular structure dynamics and spatial distributions within cells.

Current methods for image analysis in microscopy are tailored to specific biological contexts, which creates challenges in implementation and efficiency for researchers studying a diverse range of subcellular processes. Our application of dispersion indices, traditionally used in economics, offers a universal framework for high throughput quantification of biological structures, enabling easier and more consistent analysis across various biological contexts. By transforming pixel intensity and count into meaningful statistical measures, our method simplifies the quantification of subcellular structures such as autophagic puncta, microtubule dynamics, and mitochondrial clustering. This approach accelerates the quantitative analysis of sub-cellular processes in disease as well as imaged-based drug discovery. Classification: Bioengineering, Cell Biology, Applied Biological Sciences.

Long-Range Three-Dimensional Tracking of Nanoparticles Using Interferometric Scattering Microscopy.

Tracking nanoparticle movement is highly desirable in many scientific areas, and various imaging methods have been employed to achieve this goal. Interferometric scattering (iSCAT) microscopy has been particularly successful in combining very high spatial and temporal resolution for tracking small nanoparticles in all three dimensions. However, previous works have been limited to an axial range of only a few hundred nanometers. Here, we present a robust and efficient measurement and analysis strategy for three-dimensional tracking of nanoparticles at high speed and with nanometer precision. After discussing the principle of our approach using synthetic data, we showcase the performance of the method by tracking gold nanoparticles with diameters ranging from 10 to 80 nm in water, demonstrating an axial tracking range from 4 μm for the smallest particles up to over 30 μm for the larger ones. We point out the limitations and robustness of our system across various noise levels and discuss its promise for applications in cell biology and material science, where the three-dimensional motion of nanoparticles in complex media is of interest.

ARTIFICIAL INTELLIGENCE IN BIOLOGY EDUCATION

Artificial intelligence (AI) in biology education can be defined as the deployment of AI in sections of study that help learners and researchers in their areas of concentration, specifically biological sciences. It gives a significant meaning toward the improvement of biology education since AI contributes toward the improvement of research methods and the expansion of knowledge on the associated biological concepts. Continued advances in artificial intelligence as a powerful tool in biological research will prove a major boon to the further development of biology education. Traditional basic biological sciences and such interdisciplinary fields as computational biology will step up their cooperation and come up with new theoretical predictions. They develop new theoretical frameworks; all these exciting changes that are rocking the biological world today are destined to reshape the face of the education in biology in the twenty-first century.

Perspective: on the importance of extensive, high-quality and reliable deposition of biomolecular NMR data in the age of artificial intelligence.

Artificial intelligence (AI) models are revolutionising scientific data analysis but are reliant on large training data sets. While artificial training data can be used in the context of NMR processing and data analysis methods, relating NMR parameters back to protein sequence and structure requires experimental data. In this perspective we examine what the biological NMR community needs to do, in order to store and share its data better so that we can make effective use of AI methods to further our understanding of biological molecules. We argue, first, that the community should be depositing much more of its experimental data. In particular, we should be depositing more spectra and dynamics data. Second, the NMR data deposited needs to capture the full information content required to be able to use and validate it adequately. The NMR Exchange Format (NEF) was designed several years ago to do this. The widespread adoption of NEF combined with a new proposal for dynamics data specifications come at the right time for the community to expand its deposition of data. Third, we highlight the importance of expanding and safeguarding our experimental data repository, the Biological Magnetic Resonance Data Bank (BMRB), not only in the interests of NMR spectroscopists, but biological scientists more widely. With this article we invite others in the biological NMR community to champion increased (possibly mandatory) data deposition, to get involved in designing new NEF specifications, and to advocate on behalf of the BMRB within the wider scientific community.

Anatomy of the uterus of sheep of the Edilbaev breed in the age aspect

Sheep farming is one of the key branches of animal husbandry in the Russian Federation. Various measures aimed at further increasing the useful qualities in sheep breeding are based not only on breeding principles, but also on the achievements of veterinary sciences. It is quite understandable that the special attention of biological science and agricultural practice to the breed, age, and species characteristics of animals, namely to the reproduction system, allows us to scientifically and correctly raise issues in breeding, care, feeding, exploitation and reproduction. Not knowing the issues of anatomical topography, the structure of organs in animals of different age groups, it is difficult to carry out various medical manipulations in surgical or obstetric and gynecological practice. The study was conducted at the Department of Animal Anatomy of the St. Petersburg State University of Veterinary Medicine. Cadaveric material for the study was delivered to the Department of Animal Anatomy of the St. Petersburg State University of Veterinary Medicine from a farm in the Leningrad Region. Female sheep of the Edilbaev breed served as the object of the study. Three age groups were selected for the study. The study established the anatomical features of the uterus in the age aspect in sheep of the Edilbaev breed, and morphometric measurements were carried out in three age-related physiological groups (fetuses, newborns, young). Skeleto- and syntopia of all parts of the uterus was determined in different age groups. It was established that the uterus of sheep of the Edilbaevsky breed belongs to mobile organs, that is, depending on physiological conditions, it can change its topographic location points. Morphometry has determined that there is an uneven increase in the components of all parts of the uterus from the fetal period to the age stage of the young. The results of the study can be used by veterinary specialists, in particular obstetricians, surgeons to establish operative access to the uterus and in obstetric and gynecological practice in sheep breeding.

Introduction of 2024 International Conference on Advances in Biological Science and Technology, September 28-30, 2024, Online Video Conference

Introduction of 2024 International Conference on Advances in Biological Science and Technology, September 28-30, 2024, Online Video Conference

Applying Spatiotemporal Modeling of Cell Dynamics to Accelerate Drug Development.

Cells act as physical computational programs that utilize input signals to orchestrate molecule-level protein-protein interactions (PPIs), generating and responding to forces, ultimately shaping all of the physiological and pathophysiological behaviors. Genome editing and molecule drugs targeting PPIs hold great promise for the treatments of diseases. Linking genes and molecular drugs with protein-performed cellular behaviors is a key yet challenging issue due to the wide range of spatial and temporal scales involved. Building predictive spatiotemporal modeling systems that can describe the dynamic behaviors of cells intervened by genome editing and molecular drugs at the intersection of biology, chemistry, physics, and computer science will greatly accelerate pharmaceutical advances. Here, we review the mechanical roles of cytoskeletal proteins in orchestrating cellular behaviors alongside significant advancements in biophysical modeling while also addressing the limitations in these models. Then, by integrating generative artificial intelligence (AI) with spatiotemporal multiscale biophysical modeling, we propose a computational pipeline for developing virtual cells, which can simulate and evaluate the therapeutic effects of drugs and genome editing technologies on various cell dynamic behaviors and could have broad biomedical applications. Such virtual cell modeling systems might revolutionize modern biomedical engineering by moving most of the painstaking wet-laboratory effort to computer simulations, substantially saving time and alleviating the financial burden for pharmaceutical industries.

Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists.

Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated. However, data sets are often becoming so sizable that analysing them manually is increasingly burdensome and unrealistic. Fortunately, we have also seen a corresponding rise in computing power and the capability of machine learning algorithms, which offer the possibility of performing some of the analysis required for PAM automatically. Nonetheless, the field of automatic detection of acoustic events is still in its infancy in biology and ecology. In this review, we examine the trends in bioacoustic PAM applications, and their implications for the burgeoning amount of data that needs to be analysed. We explore the different methods of machine learning and other tools for scanning, analysing, and extracting acoustic events automatically from large volumes of recordings. We then provide a step-by-step practical guide for using automatic detection in bioacoustics. One of the biggest challenges for the greater use of automatic detection in bioacoustics is that there is often a gulf in expertise between the biological sciences and the field of machine learning and computer science. Therefore, this review first presents an overview of the requirements for automatic detection in bioacoustics, intended to familiarise those from a computer science background with the needs of the bioacoustics community, followed by an introduction to the key elements of machine learning and artificial intelligence that a biologist needs to understand to incorporate automatic detection into their research. We then provide a practical guide to building an automatic detection pipeline for bioacoustic data, and conclude with a discussion of possible future directions in this field.

3D bioprinting for drug development and screening: Recent trends towards personalized medicine

Personalized medicine, leveraging patient-specific genomic data, is revolutionizing treatment paradigms by enabling tailored therapies to individual needs, thus mitigating adverse effects and enhancing clinical outcomes. One innovative application in this field is the use of 3D bioprinting technology to design, develop and screen patient-customized medicines. This technology meticulously designs and develops therapeutic drugs by depositing active pharmaceutical ingredients layer by layer, culminating in a drug delivery structure optimized for the patient's unique dosage requirements. 3D bioprinting represents an emerging confluence of biological sciences and additive manufacturing, meeting the clinical demand for functional biological tissues through the integration of printing technologies, regenerative medicine principles, and advanced material science. This review provides an overview of the current state of 3D bioprinting applications and explores the transformative potential of 3D bioprinting in personalizing medicine. Moreover, the review elucidates the capabilities of 3D bioprinting in drug design, facilitating the production of complex therapeutic regimens in specific dosage forms, such as drugs with tailored dose concentrations, controlled release kinetics, and the capacity to combine multiple therapeutic regimens into a single, easy-to-administer format. This paper also addresses the challenges and obstacles faced in integrating 3D bioprinting techniques into pharmaceutical practice, ranging from technical and regulatory hurdles to scalability. Further, the efficacy of 3D bioprinting as a tool for advancing personalized medicine helps to utilize the full potential of this technology to enhance patient healthcare regimes.

A student-partnered approach to design a course-based undergraduate research experience (CURE) in biological sciences

Immersive research opportunities allow students to take ownership of their learning, explore based on curiosity, and engage in the scientific process while developing confidence and skills. However, research positions for biology undergraduates are limited, and conventional teaching labs are often restricted to pre-designed experiments without opportunities for curiosity-driven research. Course-based undergraduate research experiences (CUREs) are discovery-based research experiences that provide students with accessible avenues to explore research. Here we describe a unique student-partnered approach to the design of a foundation-level CURE in biological sciences (BIO-CURE). As student partners, we were mentored by faculty as we designed CURE projects that considered the interests and abilities of our peers to create a course structured around student-driven scientific exploration. We anticipate that this case study of our approach and experiences as the student partners of the CURE design team will serve as a helpful resource for other departments and institutions.

Pathways through which water, sanitation, hygiene, and nutrition interventions reduce antibiotic use in young children: a mediation analysis of a cluster-randomized trial.

Low-cost, household-level water, sanitation, and hygiene (WASH) and nutrition interventions can reduce pediatric antibiotic use, but the mechanism through which interventions reduce antibiotic use has not been investigated. We conducted a causal mediation analysis using data from the WASH Benefits Bangladesh cluster-randomized trial ( NCT01590095 ). Among a subsample of children within the WSH, nutrition, nutrition+WSH, and controls arms (N=1,409), we recorded caregiver-reported antibiotic use at ages 14 and 28 months and collected stool at age 14 months. Mediators included caregiver-reported child diarrhea, acute respiratory infection (ARI), and fever; and enteric pathogen carriage in stool measured by qPCR. Models controlled for mediator-outcome confounders. The receipt of any WSH or nutrition intervention reduced antibiotic use in the past month by 5.5 percentage points (95% CI 1.2, 9.9) through all pathways, from 49.5% (95% CI 45.9%, 53.0%) in the control group to 45.0 % (95% CI 42.7%, 47.2%) in the pooled intervention group. Interventions reduced antibiotic use by 0.6 percentage points (95% CI 0.1, 1.3) through reduced diarrhea, 0.7 percentage points (95% CI 0.1, 1.5) through reduced ARI with fever, and 1.8 percentage points (95% CI 0.5, 3.5) through reduced prevalence of enteric viruses. Interventions reduced antibiotic use through any mediator by 2.5 percentage points (95% CI 0.2, 5.3). Our findings bolster a causal interpretation that WASH and nutrition interventions reduced pediatric antibiotic use through reduced infections in a rural, low-income population. Bill & Melinda Gates Foundation, National Institute of Allergy and Infectious Diseases. Evidence before this study: We searched for primary studies and systematic reviews that investigated mediation of antibiotic use by water, sanitation and hygiene interventions in Scopus using (TITLE-ABS-KEY(("WASH" OR "sanitation" OR "water" OR "hygiene" OR "nutrition") AND ("antibiot*") AND ("interven*") AND ("mediat*" OR "indirect effect*" OR "pathway" OR "mechanism") AND ("use" OR "practice*")). We included all publications until September 4, 2024. We restricted results to studies in English, focused on humans, and within medicine, agricultural and biological sciences, immunology or microbiology, or environmental science. Our search yielded 115 studies. We found no relevant research studies. Four review studies discussed the need for improved sanitation and drinking water as an AMR control strategy in LMICs. Two study protocols described longitudinal observational studies in LMICs that will explore the relationship between WASH and antibiotic resistance.Added value of this study: We used causal mediation analysis to investigate mechanisms through which WASH and nutrition interventions reduced antibiotic use in young children in a community setting in rural Bangladesh. This study is rigorous because it leverages a randomized trial with high intervention adherence and includes objectively measured mediators. We found that WASH and nutrition interventions reduced antibiotic use via reduced diarrhea, ARI with fever, and enteric virus carriage. This study improves on previous studies by identifying a specific mechanism through which WASH and nutrition interventions reduced pediatric antibiotic use in an understudied setting and population.Implications of all the available evidence: In a previous analysis of a randomized trial of WASH and nutrition interventions, we found that pediatric antibiotic use was lower in the intervention arms compared to control. Here, using causal mediation analysis, we identified several biologically plausible pathways through which interventions likely reduced antibiotic use. This analysis bolsters a causal interpretation that low-cost, household-level WASH and nutrition interventions can reduce pediatric antibiotic use in settings with similar infectious disease dynamics and antimicrobial access.

Cell scientist to watch – Gokul Upadhyayula

ABSTRACT Srigokul (Gokul) Upadhyayula is Scientific Director of the Advanced BioImaging Center (ABC) and an Assistant Professor in Residence of Cell Biology, Development and Physiology at the University of California (UC), Berkeley, USA. Gokul completed his PhD in the bioengineering department with Valentine Vullev at UC Riverside, where he built his first microscope. Inspired to apply his fascination with microscopy and image analysis to understanding biological phenomena, he subsequently joined the group of Tomas Kirchhausen at Harvard Medical School as a postdoc and collaborated with Eric Betzig at Janelia Research Campus on major advances in adaptive optical lattice light-sheet microscopy (LLSM) technology to study fundamental cellular processes such as clathrin-mediated endocytosis and nuclear pore reformation. In 2019, he moved to UC Berkeley to build the ABC together with Eric Betzig. The ABC aims to enhance the accessibility and adoption of data-heavy imaging technologies like LLSM. Gokul's team combines expertise in biology, chemistry, engineering, microscopy and computational science to provide resources and tools that allow biologists to extract meaningful insights from imaging data. We spoke with Gokul over Zoom to learn about his career path, the challenges that come with handling massive bioimaging datasets and the vision of the ABC for the future of bioimaging.

Influence of School Working Conditions of Teachers’ Turnover on Learning Achievement in Biology in Public Secondary Schools in Garissa County, Kenya

The modern propensity toward multi-disciplinary research and the alignment of scientific knowledge from different fields has led to significant overlap of the field of Biological sciences with other scientific disciplines. Modern principles of other fields such as Chemistry, Medicine, and Physics are integrated with those of Biology in areas such as Biochemistry, Biomedicine, and Biophysics, making Biology a valuable subject to humankind. However, the government’s inability to retain qualified and experienced teachers in some schools due to high turnover impacts negatively on the students’ learning achievement in this subject. This paper looks into the influence of school working conditions of teachers’ turnover on learning achievement in Biology in public secondary schools in Garissa County. The study was conducted using pragmatic worldview and adopted a convergent mixed-methods design. With a target population of 2786 respondents from 40 public secondary schools, a sample size of 387 respondents was obtained. Using stratified random sampling, 27 schools were selected from which 336 students and 51 teachers of Biology were randomly and purposively drawn respectively. Data collection was done using Biology teacher questionnaire and document analysis guide. The quantitative data was analyzed using descriptive and inferential statistics while qualitative data was analyzed thematically. The study findings showed that school working conditions (r=0.73, p<0.05) had a positive significant correlation with students’ learning achievement in Biology. Qualitative data findings revealed that majority of the teachers intended to transfer from rural to urban schools on the basis of unsuitable working conditions and insecurity. The study concluded that school working conditions of teachers’ turnover influenced low learning achievement in Biology in the study area. The study therefore, recommended strategies that should lower teacher turnover and increase learning achievement in Biology as follows; school principals should provide good leadership and adequate instructional resources for effective teaching of Biology while the government should post security officers to every learning institution in Garissa County to safeguard the non-local teachers’ safety and security

Channel-facilitated transport under resetting dynamics.

The transport of particles through channels holds immense significance in physics, chemistry, and biological sciences. For instance, the motion of solutes through biological membranes is facilitated by specialized proteins that create water-filled channels. Valuable insights can be obtained by studying the transition paths of particles through a channel and gathering information on their lifetimes inside the channel as well as their exit probabilities. In a similar vein, we consider a one-dimensional model of channel-facilitated transport where a diffusive particle is subject to attractive interactions with the walls of the channel. We study the statistics of conditional and unconditional escape times in the presence of resetting-an intermittent dynamics that brings the particle back to its initial coordinate stochastically. We determine analytically the physical conditions under which such a resetting mechanism becomes beneficial for the faster escape of the particles from the channel, thus enhancing transport. Our theory has been verified with the aid of Brownian dynamics simulations for various interaction strengths and extents. The overall results presented herein highlight the scope of resetting-based strategies to be universally promising for complex transport processes of single or long molecules through biological membranes.

Two-Dimensional Hydration and Triple-Interlayer Lattice Structures in Sulfate-Intercalated Graphene Oxide Nanosheets

Sulfate anions (SO42−) are pivotal in various scientific and industrial domains, including mineralogy, biology, and materials science. While extensive research has elucidated sulfate hydration in bulk solids, liquids, and gaseous clusters, a significant gap persists in understanding sulfate interactions within two-dimensional materials, particularly graphene oxide (GO) nanosheets. This study investigates the intricate hydration phenomena and novel triple-interlayer lattice configurations that emerge from sulfate intercalation in GO nanosheets. Utilizing a straightforward methodology for obtaining precise X-ray measurements of confined nanospaces, we analyzed the temperature-dependent behavior and structural characteristics of these systems. Our findings reveal how sulfate ions modulate interlayer spacing, the dynamics of GO layers, and phase transitions. This research offers an atomic-scale understanding of hybrid hydration behaviors within confined SO4-H2O nano-environments, advancing our knowledge of sulfate interactions in two-dimensional materials.

Recent Advances in Developing Optical and Electrochemical Sensors for Monitoring Thiram and Future Perspectives.

Thiram, as one widely used dithiocarbamate pesticide, has been considered seriously detrimental to food safety and human health because of poor efficiency, nonstandard/superfluous usage, and lack of a targeting effect. Developing high-performance sensors for thirams is strongly needed. With the rapid development of chemistry, biology, and materials science, many sensors have been constructed for thiram with high sensitivity and selectivity. Regarding the energy form of the signal, recognition mode, and detection principle, recent advances in the design and construction of optical and electrochemical sensors for thiram are summarized in this review, including colorimetric, luminescent, chemiluminescent, and electrochemical sensors. The advantages and disadvantages of the sensors for thiram including sensitivity, ability to avoid interference, recognition mechanism, signal output mode, and practicability are clarified in detail. Furthermore, the challenges faced, effective restrictions, and next direction of development are proposed for achieving more sensitive and selective analysis of thiram with less interference. We desire that this review will supply a solid theoretical basis and inspiration to generate innovative thinking for achieving new progress on thiram assays and the commercialization of the developed sensors in the future.

Actividades Experimentais de Física de Baixo Custo: Um exemplo particular na 10ª Classe do curso de Ciências Físicas e Biológicas

The research focused on experimental activities, in the form of low-cost laboratory practice, with the aim of analyzing the reality of teachers in the teaching-learning process of physics in the 10th grade of the Physical and Biological Sciences course. An experimental study was carried out at the Secondary School Liceu 26 de Abril no. 1677 in the Municipality of Lubango, Huíla Province, Republic of Angola. Questionnaires were used as research tool, allowing a quantitative approach to be reported, using the statistical method in laboratory practice. There was a 100% sample of 6 teachers who teach physics and a 25% sample of 367 students. Once the diagnosis had been made, it was found that the institution in question does not have a laboratory equipped to carry out experimental activities, either with low-cost materials or with recycled materials and, of the few laboratory practices observed, there was no example of a calculation that included analysis of the theory of error for direct or indirect measurement.

Reliable deep learning in anomalous diffusion against out-of-distribution dynamics.

Anomalous diffusion plays a crucial rule in understanding molecular-level dynamics by offering valuable insights into molecular interactions, mobility states and the physical properties of systems across both biological and materials sciences. Deep-learning techniques have recently outperformed conventional statistical methods in anomalous diffusion recognition. However, deep-learning networks are typically trained by data with limited distribution, which inevitably fail to recognize unknown diffusion models and misinterpret dynamics when confronted with out-of-distribution (OOD) scenarios. In this work, we present a general framework for evaluating deep-learning-based OOD dynamics-detection methods. We further develop a baseline approach that achieves robust OOD dynamics detection as well as accurate recognition of in-distribution anomalous diffusion. We demonstrate that this method enables a reliable characterization of complex behaviors across a wide range of experimentally diverse systems, including nicotinic acetylcholine receptors in membranes, fluorescent beads in dextran solutions and silver nanoparticles undergoing active endocytosis.

Nanozyme-enhanced enzyme control: Computational strategies for precise and sustained enzymatic activity in tissue engineering

Nanomaterials are revolutionizing tissue engineering by significantly enhancing enzyme-mediated chemical processes and cellular activities, thereby advancing the development of more effective therapeutic strategies. This research delves into the intricate dynamics of enzyme diffusion within tissue matrices, providing a comprehensive computational modeling framework aimed at optimizing enzyme concentration control in tissue scaffolds. Through advanced numerical algorithms and computational analysis, the study simulates optimal control strategies for regulating nanozyme levels, ensuring precise and sustained enzymatic activity within the scaffolds. A novel aspect of this work is the integration of videographic records, which offers an enriched understanding of the complex interactions between nanomaterials and biological tissues, providing detailed insights into the system’s operational dynamics. The study bridges the gap between experimental methodologies and theoretical models, aligning with the cutting-edge research in chemical physics and physical chemistry by offering novel approaches to tissue engineering challenges. The findings underscore the critical role of nanomaterials in achieving precise enzymatic control, which is pivotal for the development of advanced biomimetic systems and improved therapeutic outcomes. This work contributes to the ongoing frontier research in physical phenomena within chemistry, biology, and materials science, by providing significant new insights into enzyme delivery mechanisms and their application in biomedical contexts. The research not only highlights the importance of innovative methodologies in tissue engineering but also sets the stage for future experimental validations and potential clinical applications

Scientific terms as a part of comparative tropes of modern Russian prose

Modern Russian prose is characterized by the active use of scientific terms from different fields of knowledge, which act as images of comparison for comparative tropes. Such tropes are marked by stylistic emphasis, as they contain new information for the addressee. Comparative tropes are defined as metaphors and similes of various structural types. The purpose of our work is to analyze the use of scientific terms as images of comparison for comparative constructions and determine their functions in the texts of modern Russian prose. The material for the study is the works of E. Vodolazkin, A. Ivanov, A. Ilichevsky, A. Matveeva, V. Pelevin, D. Rubina, A. Salnikova, O. Slavnikova, M. Stepnova, O. Vasyakina and others. The article discusses terms in tropes representing different fields of science: physics, chemistry, mathematics, medicine, biology, physiology, linguistics, literary criticism, art history, and computer science. The most active in the comparative tropes of modern prose are biological, medical, physical and computer terms. The productivity of these terms is associated with the rapid development of these areas of knowledge and their role in modern society. Regarding parts of speech, most of them are substantives. By metaphorizing, terms are subject to determinologization. In prose texts, terms often have distributors that clarify, specify or modify the image of comparison. In modern prose, scientific terms as part of tropes perform a number of functions: the function of figurative characteristics of a person, object or phenomenon, the evaluative function, the text-forming and conceptualizing functions. Scientific terms as part of metaphors and similes replenish and update the literature language.