Hasil untuk "Biology (General)"

James N. Cobley, Michalis G. Nikolaidis

Redox biology underpins signalling, metabolism, immunity, and adaptation, yet lacks a unifying theoretical framework capable of formalising structure, function, and dynamics. Current interpretations rely on descriptive catalogues of molecules and reactions, obscuring how redox behaviour emerges from constrained biochemical organisation. Here, we present a mathematical theory of redox biology that resolves this gap by treating redox systems as finite, compositional, dynamical, and spatially embedded objects. We define a structured redox state space in which admissible molecular transformations form a neutral algebra of possibilities. Biological function emerges when this structure is embedded within a wider molecular network and interpreted through weighted flux distributions. Time-dependent reweighting of these transformations generates redox dynamics, while spatial embedding enforces locality and causality, yielding a distributed redox field. Within this framework, context dependence, nonlinearity, hysteresis, and memory arise naturally from bounded state spaces and irreversible transformations, without requiring ad hoc assumptions. This theory provides a working, predictive interpretative basis for redox biology: it constrains admissible states and trajectories, clarifies the meaning of redox measurements, and links chemical transformation to biological behaviour. Redox biology emerges as a geometric, dynamical process governed by lawful organisation.

Seungjae Han, Taeyoung Oh, Soohyeon Lee et al.

With continuous advancements in autonomous driving technology, systematic and reliable safety verification is becoming increasingly important. However, despite the active development of various X-in-the-loop simulation (XILS) platforms to validate autonomous driving systems (ADSs), standardized evaluation frameworks for assessing the credibility of the simulation platforms themselves remain lacking. Therefore, we propose a novel integrated credibility-assessment methodology that combines dynamics-based fidelity assessment, parameter-based reliability assessment, and scenario-based reliability assessment. These three techniques evaluate the similarity and consistency between XILS and real-world test data based on statistical and mathematical comparisons. The three consistency measures are then utilized to derive a dynamics-based correlation metric for fidelity, along with parameter-based and scenario-based correlation and applicability metrics for reliability. The novel contribution of this paper lies in a geometric similarity analysis methodology that significantly enhances the efficiency of credibility assessment. We propose a methodology that enables geometric similarity assessment through spider chart visualization of metrics derived from the credibility-assessment process and shape comparison, based on Procrustes, Fréchet, and Hausdorff distances. As a result, speed is not a dominant factor for credibility evaluation, enabling assessment with a single representative speed test; the framework simplifies the XILS evaluation and enhances ADS validation efficiency.

Nathan S. Babcock, Brandy N. Babcock

This technical monograph provides a comprehensive overview of the field of quantum biology. It approaches quantum biology from a physical perspective with core quantum mechanical concepts presented foremost to provide a theoretical foundation for the field. An extensive body of research is covered to clarify the significance of quantum biology as a scientific field, outlining the field's long-standing importance in the historical development of quantum theory. This lays the essential groundwork to enable further advances in nanomedicine and biotechnology. Written for academics, biological science researchers, physicists, biochemists, medical technologists, and students of quantum mechanics, this text brings clarity to fundamental advances being made in the emerging science of quantum biology.

Dulce V Ramírez-Rodríguez, Christine J Band-Schmidt, Bárbara González-Acosta et al.

Gymnodinium catenatum, un dinoflagelado productor de toxinas paralizantes, es una de las especies de dinoflagelados más estudiadas, sin embargo, poco se conoce sobre sus interacciones con bacterias. En este estudio se determinó el efecto de un consorcio bacteriano en cepas de G. catenatum. Se emplearon 4 cepas de G. catenatum aisladas de Bahía de Santiago, Colima; Lázaro Cárdenas, Michoacán; Bahía Concepción y Bahía de La Paz, Baja California Sur. De la cepa originaria de la costa de Colima se aisló un consorcio bacteriano compuesto de 3 bacterias empleando placas de agar con medio GSe. Se incubaron a una salinidad de 34, a 24 °C y en un ciclo de 12:12 h L:O. Gymnodinum catenatum se cultivó en medio GSe líquido en las mismas condiciones que las bacterias. La interacción entre los microorganismos se evaluó inoculando el consorcio bacteriano en las cepas de G. catenatum. Se determinó la abundancia máxima, las tasas de crecimiento, la longitud de cadenas y el perfil de pigmentos y toxinas paralizantes en G. catenatum en presencia y ausencia del consorcio bacteriano. En presencia de las bacterias no se observó la misma respuesta en el crecimiento de las cepas de G. catenatum, ni en el perfil y contenido de toxinas, pero sí se observó un incremento significativo en el número de células en cadena. También se notaron cambios en la morfología de G. catenatum. Además, en presencia del consorcio bacteriano, en la mayoría de las cepas incrementó el contenido de pigmentos accesorios. Los resultados sugieren que el consorcio bacteriano pudiera ser un agente estresante para G. catenatum proporcionando nuevas perspectivas de interacción entre las bacterias y G. catenatum.

Giuliano Colosimo, Gwyneth Montemuro, Gregory A. Lewbart et al.

As the analysis of blood metabolites has become more readily accessible thanks to the use of point-of-care analyzers, it is now possible to evaluate stress level of wild animals directly in the field. Lactate is receiving much attention as a good stress level proxy in individuals subjected to capture, manual restraint, and data sampling in the wild, and appropriate protocols to maintain lactate values low should be preferred. In this study we compare how two different capture methodologies, hand grab vs. noose pole, affect the variation of blood lactate values in Cyclura carinata iguanas when captured for sampling. We used blood lactate concentration, measured immediately upon- and 15 min after-capture, as a proxy for stress level. While the primary goal of this work is to determine the least stressful capture methodology to be favored when sampling this and other wild iguanas, we also evaluated additional baseline physiological parameters relevant to the health and disease monitoring for this species. Our results show that while initial lactate values level-out in sampled individuals after 15 min in captivity, regardless of the capture methodology, rock iguanas captured by noose pole showed significantly higher lactate concentration and increased heartbeat rate immediately after capture. While the overall health evaluation determined that all analyzed individuals were in good health, based on our results we recommend that, when possible, hand capture should be preferred over noose pole when sampling wild individuals.

Limin Chuan, Jingjuan Zhao, Shijie Qi et al.

From the perspective of project and paper datasets, research frontier recognition in the field of agricultural resources and the environment using the Latent Dirichlet Allocation (LDA) topic extraction model was studied. By combining the wisdom of domain experts to judge the similarities and differences of clustering topics between the two data sources, multidimensional indicators, such as the emerging degree, attention degree, innovation degree, and intersection degree, were comprehensively constructed for frontier identification. The methods for hot research frontiers, emerging research frontiers, extinction research frontiers, and potential research frontiers were proposed. The empirical research in the field of agricultural resources and the environment showed that the “interaction mechanism of plant–rhizosphere–microbial diversity” was a hot research frontier in the years 2016–2021. The themes of “wastewater treatment technology and efficient utilization of water resources”, the “value-added utilization of agricultural wastes and sustainable development”, the “soil ecological response mechanism under agronomic management measures”, and the “mechanism of soil landslide, erosion, degradation and prediction evaluation” were judged as potential research frontiers. The theme of “ecosystems management and pollution control of agricultural and animal husbandry” was recognized as an emerging research frontier. The results confirm that the fusion method of extracting topics from project and paper data, combined with expert intelligence and frontier indicators for fine classification of frontiers, is an optional approach. This study provides strong support for accurately identifying the forefront of scientific research, grasping the latest research progress, efficiently allocating scientific and technological resources, and promoting technological innovation.

Felix E Y Aggor, Martinna Bertolini, Bianca M Coleman et al.

Oropharyngeal candidiasis (OPC) is the most common human fungal infection, arising typically from T cell immune impairments. IL-17 and IL-22 contribute individually to OPC responses, but here we demonstrate that the combined actions of both cytokines are essential for resistance to OPC. Mice lacking IL-17RA and IL-22RA1 exhibited high fungal loads in esophagus- and intestinal tract, severe weight loss, and symptoms of colitis. Ultimately, mice succumbed to infection. Dual loss of IL-17RA and IL-22RA impaired expression of small proline rich proteins (SPRRs), a class of antimicrobial effectors not previously linked to fungal immunity. Sprr2a1 exhibited direct candidacidal activity in vitro, and Sprr1-3a-/- mice were susceptible to OPC. Thus, cooperative actions of Type 17 cytokines mediate oral mucosal anti-Candida defenses and reveal a role for SPRRs.

Eran Agmon

Composition is a powerful principle for systems biology, focused on the interfaces, interconnections, and orchestration of distributed processes to enable integrative multiscale simulations. Whereas traditional models focus on the structure or dynamics of specific subsystems in controlled conditions, compositional systems biology aims to connect these models, asking critical questions about the space between models: What variables should a submodel expose through its interface? How do coupled models connect and translate across scales? How do domain-specific models connect across biological and physical disciplines to drive the synthesis of new knowledge? This approach requires robust software to integrate diverse datasets and submodels, providing researchers with tools to flexibly recombine, iteratively refine, and collaboratively expand their models. This article offers a comprehensive framework to support this vision, including: a conceptual and graphical framework to define interfaces and composition patterns; standardized schemas that facilitate modular data and model assembly; biological templates that integrate detailed submodels that connect molecular processes to the emergence of the cellular interface; and user-friendly software interfaces that empower research communities to construct and improve multiscale models of cellular systems. By addressing these needs, compositional systems biology will foster a unified and scalable approach to understanding complex cellular systems.

Lingxia Qiao, Ali Khalilimeybodi, Nathaniel J Linden-Santangeli et al.

Understanding the mechanisms of interactions within cells, tissues, and organisms is crucial to driving developments across biology and medicine. Mathematical modeling is an essential tool for simulating biological systems and revealing biochemical regulatory mechanisms. Building on experiments, mechanistic models are widely used to describe small-scale intracellular networks and uncover biochemical mechanisms in healthy and diseased states. The rapid development of high-throughput sequencing techniques and computational tools has recently enabled models that span multiple scales, often integrating signaling, gene regulatory, and metabolic networks. These multiscale models enable comprehensive investigations of cellular networks and thus reveal previously unknown disease mechanisms and pharmacological interventions. Here, we review systems biology models from classical mechanistic models to larger, multiscale models that integrate multiple layers of cellular networks. We introduce several examples of models of hypertrophic cardiomyopathy, exercise, and cancer cell proliferation. Additionally, we discuss methods that increase the certainty and accuracy of model predictions. Integrating multiscale models has become a powerful tool for understanding disease and inspiring drug discoveries by incorporating omics data within the cell and across tissues and organisms.

Angel Chao, Ren-Chin Wu, Chiao-Yun Lin et al.

Small cell neuroendocrine carcinoma of the cervix (SCNECC) is an uncommon but aggressive uterine malignancy, the cause of which is generally associated with human papillomavirus (HPV) infection. A lack of clinical trials and evidence-based treatment guidelines poses therapeutic challenges to this rare tumor. At present, published data remain limited to case series and case reports. While clinical management has traditionally followed those of small cell neuroendocrine (SCNE) lung cancer relying on surgery, chemoradiation, and systemic chemotherapy, the prognosis remains dismal. Immune checkpoint inhibitors (ICIs), such as monoclonal antibodies that target programmed death-1 (PD-1) or programmed death-ligand 1 (PD-L1), atezolizumab and durvalumab have proven effective in extensive-stage SCNE lung cancer. Moreover, pembrolizumab has also proven beneficial effects when added onto chemotherapy in metastatic and recurrent HPV-associated non-SCNE cervical cancer. It holds promise to use ICIs in combination with chemoradiation to improve the clinical outcomes of patients with SCNECC. Future advances in our understanding of SCNECC biology – associated with the study of its genomic and molecular aberrations as well as knowledge from SCNE of lung and other extrapulmonary sites– would be helpful in discovering new molecular targets for drug development. Collaborative efforts and establishment of a SCNECC-specific biobank will be essential to achieve this goal.

Ho Soon Choi

This study aims to produce renewable energy by applying a solar-energy-harvesting architectural design using solar panels on the facade of a building. To install as many solar panels as possible on the building elevation, the Signal Box auf dem Wolf, located in Basel, Switzerland, was selected as the research target. The solar panels to be installed on the facade of the Signal Box auf dem Wolf are planned such that they are able to move according to the optimal tilt angle every month to allow maximal energy generation. The kinetic photovoltaic facade system and the simulation of renewable energy generation were implemented using a parametric design. The novelty of this study is the development of a kinetic photovoltaic facade system using a parametric design algorithm. From the perspective of renewable energy in the field of architecture, the kinetic photovoltaic facade system developed in this study has the advantage of producing maximal renewable energy according to the optimal tilt angle of the solar panels. Additionally, building facades that move according to the optimal tilt angle will contribute to the expansion of the field of sustainable architectural design.

Yue Wang

Cancer, as the uncontrollable cell growth, is related to many branches of biology. In this review, we will discuss three mathematical approaches for studying cancer biology: population dynamics, gene regulation, and developmental biology. If we understand all biochemical mechanisms of cancer cells, we can directly calculate how the cancer cell population behaves. Inversely, just from the cell count data, we can use population dynamics to infer the mechanisms. Cancer cells emerge from certain genetic mutations, which affect the expression of other genes through gene regulation. Therefore, knowledge of gene regulation can help with cancer prevention and treatment. Developmental biology studies acquisition and maintenance of normal cellular function, which is inspiring to cancer biology in the opposite direction. Besides, cancer cells implanted into an embryo can differentiate into normal tissues, which provides a possible approach of curing cancer. This review illustrates the role of mathematics in these three fields: what mathematical models are used, what data analysis tools are applied, and what mathematical theorems need to be proved. We hope that applied mathematicians and even pure mathematicians can find meaningful mathematical problems related to cancer biology.

Mohammed Alyahya, Taghrid Aloraini, Youseef Al-Harbi et al.

Background: Physicians and geneticists face challenges in making accurate diagnoses during clinical evaluations; affecting patients and clinicians. The aim of this study was to estimate the hit rate of the non-consanguineous population. Moreover, prevalence of the genetic disorder in both the consanguineous and non- consanguineous population of Saudi Arabia at King Abdulaziz Medical City in Riyadh data. Methods: We reviewed 681 families and 1563 individuals with 2,565,335 variants in the King Abdullah International Medical Research Center (KAIMRC) Genomic Database (KGD), Riyadh, Saudi Arabia. All the ES requests were obtained from the physician and clinical geneticist of KAIMRC, and the test was performed either in-house or in a College of American Pathologists accredited laboratory center for clinical purposes. Results: A total of 151 non-consanguineous individuals with exome sequencing requests in the population genomic database of King Abdullah International Medical Research Center was considered for the study. In total, 27 had disease-causing variants, and the hit rate was 27/151 (18%). Among the 28 different variants in the 27 individuals, 50% were de novo variants and 50% inherited. The hit rate of the variants causing autosomal recessive disorders was 12/28 (42.8%), autosomal dominant disorders 13/28 (46.4%), and X-linked disorders 3/28 (10.7%). Conclusion: Non-consanguineous marriages have a lower risk of genetic disorders, and reducing consanguinity reduces the risk of genetic disorders by two to three times.   [JBCGenetics 2022; 5(2.000): 37-42]

Anne Helness, Jennifer Fraszczak, Charles Joly-Beauparlant et al.

K. Xenou, C. Sotiropoulou, P. Karousi et al.

Sanjay Mishra, Manish Charan, Ajeet Kumar Verma et al.

Recent studies revealed that ethnic differences in mechanistic target of rapamycin (mTOR) and extracellular signal-regulated kinase (ERK-1/2) signaling pathways might be associated with the development and progression of different human malignancies. The African American (AA) population has an increased rate of cancer incidence and mortality compared to the Caucasian American (CA) population. Although the socioeconomic differences across different ethnic groups contribute to the disparity in developing different cancers, recent scientific evidence indicates the association of molecular and genetic variations in racial disparities of different human malignancies. The mTOR and ERK-1/2 signaling pathways are one of the well-known oncogenic signaling mechanisms that regulate diverse molecular and phenotypic aspects of normal as well as cancer cells in response to different external or internal stimuli. To date, very few studies have been carried out to explore the significance of racial disparity with abnormal mTOR and ERK-1/2 kinase signaling pathways, which may contribute to the development of aggressive human cancers. In this review, we discuss the differential regulation of mTOR and ERK-1/2 kinase signaling pathways across different ethnic groups, especially between AA and CA populations. Notably, we observed that key signaling proteins associated with mTOR and ERK-1/2 pathway including transforming growth factor-beta (TGF-β), Akt, and VEGFR showed racially disparate expression in cancer patients. Overall, this review article encompasses the significance of racially disparate signaling molecules related to mTOR/ERK1/2 and their potential in developing tailor-made anti-cancer therapies.

Jiuxin Wang, Yutian Luo, Zhengming Yang et al.

In order to improve the measurement speed and prediction accuracy of unconventional reservoir parameters, the deep neural network (DNN) is used to predict movable fluid percentage of unconventional reservoirs. The Adam optimizer is used in the DNN model to ensure the stability and accuracy of the model in the gradient descent process, and the prediction effect is compared with the back propagation neural network (BPNN), K-nearest neighbor (KNN), and support vector regression model (SVR). During network training, L₂ regularization is used to avoid over-fitting and improve the generalization ability of the model. Taking nuclear magnetic resonance (NMR) T₂ spectrum data of laboratory unconventional core as input features, the influence of model hyperparameters on the prediction accuracy of reservoir movable fluids is also experimentally analyzed. Experimental results show that, compared with BPNN, KNN, and SVR, the deep neural network model has a better prediction effect on movable fluid percentage of unconventional reservoirs; when the model depth is five layers, the prediction accuracy of movable fluid percentage reaches the highest value, the predicted value of the DNN model is in high agreement with the laboratory measured value. Therefore, the movable fluid percentage prediction model of unconventional oil reservoirs based on the deep neural network model can provide certain guidance for the intelligent development of the laboratory’s reservoir parameter measurement.

Robyn P. Araujo, Sean T. Vittadello, Michael P. H. Stumpf

Innovation in synthetic biology often still depends on large-scale experimental trial-and-error, domain expertise, and ingenuity. The application of rational design engineering methods promise to make this more efficient, faster, cheaper and safer. But this requires mathematical models of cellular systems. And for these models we then have to determine if they can meet our intended target behaviour. Here we develop two complementary approaches that allow us to determine whether a given molecular circuit, represented by a mathematical model, is capable of fulfilling our design objectives. We discuss algebraic methods that are capable of identifying general principles guaranteeing desired behaviour; and we provide an overview over Bayesian design approaches that allow us to choose from a set of models, that model which has the highest probability of fulfilling our design objectives. We discuss their uses in the context of biochemical adaptation, and then consider how robustness can and should affect our design approach.

Joachim Kloehn, Rebecca D. Oppenheim, Ghizal Siddiqui et al.

Abstract Background Acetyl-CoA is a key molecule in all organisms, implicated in several metabolic pathways as well as in transcriptional regulation and post-translational modification. The human pathogen Toxoplasma gondii possesses at least four enzymes which generate acetyl-CoA in the nucleo-cytosol (acetyl-CoA synthetase (ACS); ATP citrate lyase (ACL)), mitochondrion (branched-chain α-keto acid dehydrogenase-complex (BCKDH)) and apicoplast (pyruvate dehydrogenase complex (PDH)). Given the diverse functions of acetyl-CoA, we know very little about the role of sub-cellular acetyl-CoA pools in parasite physiology. Results To assess the importance and functions of sub-cellular acetyl-CoA-pools, we measured the acetylome, transcriptome, proteome and metabolome of parasites lacking ACL/ACS or BCKDH. We demonstrate that ACL/ACS constitute a synthetic lethal pair. Loss of both enzymes causes a halt in fatty acid elongation, hypo-acetylation of nucleo-cytosolic and secretory proteins and broad changes in gene expression. In contrast, loss of BCKDH results in an altered TCA cycle, hypo-acetylation of mitochondrial proteins and few specific changes in gene expression. We provide evidence that changes in the acetylome, transcriptome and proteome of cells lacking BCKDH enable the metabolic adaptations and thus the survival of these parasites. Conclusions Using multi-omics and molecular tools, we obtain a global and integrative picture of the role of distinct acetyl-CoA pools in T. gondii physiology. Cytosolic acetyl-CoA is essential and is required for the synthesis of parasite-specific fatty acids. In contrast, loss of mitochondrial acetyl-CoA can be compensated for through metabolic adaptations implemented at the transcriptional, translational and post-translational level.

Kai Aoki, Takehito Sugasawa, Kouki Yanazawa et al.

Background With the rapid progress of genetic engineering and gene therapy methods, the World Anti-Doping Agency has raised concerns regarding gene doping, which is prohibited in sports. However, there is no standard method available for detecting transgenes delivered by injection of naked plasmids. Here, we developed a detection method for detecting transgenes delivered by injection of naked plasmids in a mouse model that mimics gene doping. Methods Whole blood from the tail tip and one piece of stool were used as pre-samples of injection. Next, a plasmid vector containing the human erythropoietin (hEPO) gene was injected into mice through intravenous (IV), intraperitoneal (IP), or local muscular (IM) injection. At 1, 2, 3, 6, 12, 24, and 48 h after injection, approximately 50 µL whole blood was collected from the tail tip. One piece of stool was collected at 6, 12, 24, and 48 h. From each sample, total DNA was extracted and transgene fragments were analyzed by Taqman quantitative PCR (qPCR) and SYBR green qPCR. Results In whole blood DNA samples evaluated by Taqman qPCR, the transgene fragments were detected at all time points in the IP sample and at 1, 2, 3, 6, and 12 h in the IV and IM samples. In the stool-DNA samples, the transgene fragments were detected at 6, 12, 24, and 48 h in the IV and IM samples by Taqman qPCR. In the analysis by SYBR green qPCR, the transgene fragments were detected at some time point in both specimens; however, many non-specific amplicons were detected. Conclusions These results indicate that transgene fragments evaluated after each injection method of naked plasmids were detected in whole-blood and stool DNA samples. These findings may facilitate the development of methods for detecting gene doping.