This article frames the relation between biology and physics by characterizing the former as a subdiscipline rather than a special case of the latter. To do this, we posit biological physics as the science of living matter in contrast to classic biophysics, the study of organismal properties by physical techniques. At the scale of the individual cell, living matter is nonunitary, i.e., not composed of aggregated subunits, and has features (e.g., intracellular organizational arrangements and biomolecular condensates) that are unlike any materials of the nonliving world. In transiently or constitutively multicellular forms (social microorganisms, animals, plants), living matter sustains physical processes that are generic (shared with nonliving matter, e.g., subunit communication by molecular diffusion in cellular slime molds), biogeneric (analogous to nonliving matter but realized through cellular activities, e.g., subunit demixing in animal embryos) or nongeneric (pertaining to sui generis materials, e.g., budding of active solids in plants). This "forms of matter" perspective is philosophically situated in the dialectical materialism of Engels and Hessen and the multilevel physicalism of Neurath and the logical empiricists. We counterpose this view to informationism and to genetic and other hierarchically reductionist physical theories of biological systems and highlight open questions regarding incompletely characterized and enigmatic forms of living matter.
Alexis Chevalier, Soumya Ghosh, Urvi Awasthi
et al.
Understanding the biological mechanisms of disease is crucial for medicine, and in particular, for drug discovery. AI-powered analysis of genome-scale biological data holds great potential in this regard. The increasing availability of single-cell RNA sequencing data has enabled the development of large foundation models for disease biology. However, existing foundation models only modestly improve over task-specific models in downstream applications. Here, we explored two avenues for improving single-cell foundation models. First, we scaled the pre-training data to a diverse collection of 116 million cells, which is larger than those used by previous models. Second, we leveraged the availability of large-scale biological annotations as a form of supervision during pre-training. We trained the \model family of models comprising six transformer-based state-of-the-art single-cell foundation models with 70 million, 160 million, and 400 million parameters. We vetted our models on several downstream evaluation tasks, including identifying the underlying disease state of held-out donors not seen during training, distinguishing between diseased and healthy cells for disease conditions and donors not seen during training, and probing the learned representations for known biology. Our models showed substantial improvement over existing works, and scaling experiments showed that performance improved predictably with both data volume and parameter count.
Jose Ignacio Arroyo, Pablo A. Marquet, Christopher P. Kempes
et al.
We developed a theory showing that under appropriate normalizations and rescalings, temperature response curves show a remarkably regular behavior and follow a general, universal law. The impressive universality of temperature response curves remained hidden due to various curve-fitting models not well-grounded in first principles. In addition, this framework has the potential to explain the origin of different scaling relationships in thermal performance in biology, from molecules to ecosystems. Here, we summarize the background, principles and assumptions, predictions, implications, and possible extensions of this theory.
Jingying Zhang, Aashish Bhatt, Grigory Maksaev
et al.
Abstract The mechanosensitive channel of small conductance (MscS) from E. coli (EcMscS) has served as the prevailing model system for understanding mechanotransduction in ion channels. Trypanosoma cruzi, the protozoan parasite causing Chagas disease, encodes a miniature MscS ortholog (TcMscS) critical for parasite development and infectivity. TcMscS contains a minimal portion of the canonical EcMscS fold yet maintains mechanosensitive channel activity, thus presenting a unique model system to assess the essential molecular determinants underlying mechanotransduction. Using cryo-electron microscopy and molecular dynamics simulations, we show that TcMscS contains two short membrane-embedded helices that would not fully cross an intact lipid bilayer. Consequently, drastic membrane deformation is induced at the protein-lipid interface, resulting in a funnel-shaped bilayer surrounding the channel. Resident lipids within the central pore lumen block ion permeation pathway, and their departure driven by lateral membrane tension is required for ion conduction. Together with electrophysiology and mutagenesis studies, our results support a direct lipid-mediated mechanical gating transition. Moreover, these findings provide a foundation for the development of alternative treatment of Chagas disease by inhibition of the TcMscS channel.
Introduction:
Fully detect risks of nerve damage, which can lead to temporary or permanent issues. Cone-beam computed tomography (CBCT) offers a three-dimensional (3D) view, providing more detailed visualisation of anatomical structures and their spatial relationships, which improves the accuracy of predicting nerve exposure. The study aims to evaluate and compare these imaging techniques’ effectiveness in categorising the relationship between third molars and the inferior alveolar canal, emphasising the importance of precise imaging for safer surgical outcomes.
Materials and Methods:
A pilot study involving 20 patients, representing 10% of the total sample size of 200, was conducted at Ahmedabad Dental College’s Department of Oral Medicine and Radiology. Investigators, trained to interpret radiological images from orthopantomography (OPG) and CBCT, compared their interpretations with those of two experts. A high inter-rater reliability was confirmed with a kappa statistic of 0.98. Following ethical approval, data were retrospectively collected from 20 cases, with digital OPG and CBCT images analysed and classified according to established criteria.
Results:
The results revealed a significant association between the results diagnosed through OPG and CBCT indicating similarity in their diagnosis. It was also seen that there was no bias towards the gender and the distribution was similar in case of diagnosis through OPG or CBCT.
Conclusion:
CBCT demands an in-depth understanding of anatomy and pathology, coupled with proficiency in operating imaging software and the ability to identify abnormalities in cross-sectional images. When executed and interpreted accurately, CBCT proves to be an exceptionally valuable tool in clinical dental practice. Its detailed 3D imaging capabilities enhance the assessment of complex cases, such as those involving intricate anatomical structures and pathologies. By providing comprehensive views that surpass traditional two-dimensional imaging, CBCT aids in precise diagnosis and treatment planning, making it an indispensable resource for addressing various dental conditions effectively.
ObjectivesSpingosine-1-phosphate (S1P) and ceramides are bioactive sphingolipids that influence cancer cell fate. Anti-ceramide antibodies might inhibit the effects of ceramide. The aim of this study was to assess the potential role of circulating S1P and anti-ceramide antibody as biomarkers in non-small cell lung cancer (NSCLC).MethodsWe recruited 66 subjects (34 controls and 32 patients with NSCLC). Patient history and clinical variables were taken from all participants. Venous blood samples were collected to evaluate plasma biomarkers. If bronchoscopy was performed, bronchial washing fluid (BWF) was also analyzed. We measured the levels of S1P and anti-ceramide antibody with ELISA.ResultsS1P levels were significantly higher in the NSCLC group (3770.99 ± 762.29 ng/mL vs. 366.53 ± 249.38 ng/mL, patients with NSCLC vs. controls, respectively, p < 0.001). Anti-ceramide antibody levels were significantly elevated in the NSCLC group (278.70 ± 19.26 ng/mL vs. 178.60 ± 18 ng/mL, patients with NSCLC vs. controls, respectively, p = 0.007). Age or BMI had no significant effect on anti-ceramide antibody or S1P levels. BWF samples had higher levels of anti-ceramide antibody (155.29 ± 27.58 ng/mL vs. 105.87 ± 9.99 ng/mL, patients with NSCLC vs. controls, respectively, p < 0.001). Overall survival (OS) was 13.36 months. OS was not affected by anti-ceramide antibody or S1P levels.ConclusionHigher levels of S1P and anti-ceramide antibody were associated with active cancer. These results suggest that sphingolipid alterations might be important features of NSCLC.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Pathology
Abstract The presence of bacterial biofilms and the occurrence of excessive inflammatory response greatly imped the healing process of chronic wounds in diabetic patients. However, effective strategies to simultaneously address these issues are still lacking. Here, a microenvironment‐adaptive nanodecoy (GC@Pd) is constructed via the coordination and in situ reduction of palladium ions on gallic acid‐modified chitosan (GC) to promote wound healing by synergistic biofilm eradication, inflammation alleviation, and immunoregulation. During the weakly acidic conditions of the biofilm infection stage, GC@Pd serves as a nanodecoy to induce bacterial aggregation. Subsequently, through its oxidase‐like activity generating reactive oxygen species and the hyperthermia from photothermal effects, it effectively eliminates the biofilm. As the local microenvironment of diabetic wounds transitions to an alkaline inflammatory state, the enzyme‐like activity of GC@Pd adapts to catalase‐like activity, effectively eliminating reactive oxygen species at the site of inflammation. Additionally, GC@Pd could selectively capture pro‐inflammatory cytokines through Michael addition reactions. In vivo experiments and transcriptomic analysis confirmed that GC@Pd could accelerate the wound transition from inflammatory to proliferative phase by eliminating biofilm infection and reducing the inflammatory response, thus promoting diabetic chronic wound healing. The nanodecoy provides a potential therapeutic strategy for treating biofilm‐infected diabetic chronic wounds.
Pavel Pereslavtsev, Christian Bachmann, Joelle Elbez-Uzan
et al.
There is widespread use of nuclear radiation for medical imagery and treatments. Worldwide, almost 40 million treatments are performed per year. There are also applications of radiation sources in other commercial fields, e.g., for weld inspection or steelmaking processes, in consumer products, in the food industry, and in agriculture. The large number of neutrons generated in a fusion reactor such as DEMO could potentially contribute to the production of the required radioactive isotopes. The associated commercial value of these isotopes could mitigate the capital investments and operating costs of a large fusion plant. The potential of producing various radioactive isotopes was studied from material pieces arranged inside a DEMO equatorial port plug. In this location, they are exposed to an intensive neutron spectrum suitable for a high isotope production rate. For this purpose, the full 3D geometry of one DEMO toroidal sector with an irradiation chamber in the equatorial port plug was modeled with an MCNP code to perform neutron transport simulations. Subsequent activation calculations provide detailed information on the quality and composition of the produced radioactive isotopes. The technical feasibility and the commercial potential of the production of various isotopes in the DEMO port are reported.
In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of the Transformer model. A series of experiments on key tasks, such as financial behavior detection and stock price prediction, were conducted to validate the effectiveness of the model. The results demonstrate that the Merkle-Transformer significantly outperforms existing deep learning models (such as RoBERTa and BERT) across performance metrics, including precision, recall, accuracy, and F1 score. In particular, in the task of stock price prediction, the performance is notable, with nearly all evaluation metrics scoring above 0.9. Moreover, the performance of the model across various hardware platforms, as well as the security performance of the proposed method, were investigated. The Merkle-Transformer exhibits exceptional performance and robust data security even in resource-constrained environments across diverse hardware configurations. This research offers a new perspective, underscoring the importance of considering data security in financial data processing and confirming the superiority of integrating data verification mechanisms in deep learning models for handling financial data. The core contribution of this work is the first proposition and empirical demonstration of a financial data analysis model that fuses data integrity verification with efficient data processing, providing a novel solution for the fintech domain. It is believed that the widespread adoption and application of the Merkle-Transformer model will greatly advance innovation in the financial industry and lay a solid foundation for future research on secure financial data processing.
Systems biology relies on mathematical models that often involve complex and intractable likelihood functions, posing challenges for efficient inference and model selection. Generative models, such as normalizing flows, have shown remarkable ability in approximating complex distributions in various domains. However, their application in systems biology for approximating intractable likelihood functions remains unexplored. Here, we elucidate a framework for leveraging normalizing flows to approximate complex likelihood functions inherent to systems biology models. By using normalizing flows in the Simulation-based inference setting, we demonstrate a method that not only approximates a likelihood function but also allows for model inference in the model selection setting. We showcase the effectiveness of this approach on real-world systems biology problems, providing practical guidance for implementation and highlighting its advantages over traditional computational methods.
Ibrahim Aldulijan, Jacob Beal, Sonja Billerbeck
et al.
Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, reduces engineering flexibility, and impairs predictability and design reuse. Functional Synthetic Biology aims to overcome these impediments by focusing the design of biological systems on function, rather than on sequence. This reorientation will decouple the engineering of biological devices from the specifics of how those devices are put to use, requiring both conceptual and organizational change, as well as supporting software tooling. Realizing this vision of Functional Synthetic Biology will allow more flexibility in how devices are used, more opportunity for reuse of devices and data, improvements in predictability, and reductions in technical risk and cost.
Nathaniel J. Linden, Boris Kramer, Padmini Rangamani
Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and '-omics' studies that have helped populate protein-protein interaction networks in great detail, modeling in systems biology lacks systematic methods to estimate kinetic parameters and quantify associated uncertainties. This is because of multiple reasons, including sparse and noisy experimental measurements, lack of detailed molecular mechanisms underlying the reactions, and missing biochemical interactions. Additionally, the inherent nonlinearities with respect to the states and parameters associated with the system of differential equations further compound the challenges of parameter estimation. In this study, we propose a comprehensive framework for Bayesian parameter estimation and complete quantification of the effects of uncertainties in the data and models. We apply these methods to a series of signaling models of increasing mathematical complexity. Systematic analysis of these dynamical systems showed that parameter estimation depends on data sparsity, noise level, and model structure, including the existence of multiple steady states. These results highlight how focused uncertainty quantification can enrich systems biology modeling and enable additional quantitative analyses for parameter estimation.
Stephen D. Williams, Tunde M. Smith, LaMonica V. Stewart
et al.
Physiological changes such as hypoxia in the tumor microenvironment (TME) endow cancer cells with malignant properties, leading to tumor recurrence and rapid progression. Here, we assessed the effect of hypoxia (1% Oxygen) on the tumor suppressor Annexin A6 (AnxA6) and the response of triple-negative breast cancer (TNBC) cells to epidermal growth factor receptor (EGFR) and androgen receptor (AR) targeted therapies. We demonstrate that brief exposure of TNBC cells to hypoxia (within 24 h) is associated with down regulation of AnxA6 while > 24 h exposure cell type dependently stimulated the expression of AnxA6. Hypoxia depicted by the expression and stability of HIF-1/2α led to up regulation of the HIF target genes <i>SLC2A1</i>, <i>PGK1</i> as well as AR and the AR target genes <i>FABP-4</i> and <i>PPAR-γ</i>, but the cellular levels of AnxA6 protein decreased under prolonged hypoxia. Down regulation of AnxA6 in TNBC cells inhibited, while AnxA6 over expression enhanced the expression and cellular levels of HIF-1/2α, <i>SLC2A1</i> and <i>PGK1</i>. RNAi mediated inhibition of hypoxia induced AnxA6 expression also strongly inhibited glucose uptake and ROS production in AnxA6 expressing TNBC cells. Using a luciferase reporter assay, we confirm that short-term exposure of cells to hypoxia inhibits while prolonged exposure of cells to hypoxia enhances AnxA6 promoter activity in HEK293T cells. Compared to cells cultured under normoxia, TNBC cells were more resistant to lapatinib under hypoxic conditions, and the downregulation of AnxA6 sensitized the cells to EGFR as well as AR antagonists. These data suggest that AnxA6 is a hypoxia inducible gene and that targeting AnxA6 upregulation may be beneficial in overcoming TNBC resistance to EGFR and/or AR targeted therapies.
Rice blast, caused by Magnaporthe oryzae (M. oryzae), is one of the most destructive diseases threatening rice production worldwide. Development of resistant cultivars using broad-spectrum resistance (R) genes with high breeding value is the most effective and economical approach to control this disease. In this study, the breeding potential of Pigm gene in geng/japonica rice breeding practice in Jiangsu province was comprehensively evaluated. Through backcross and marker-assisted selection (MAS), Pigm was introduced into two geng rice cultivars (Wuyungeng 32/WYG32 and Huageng 8/HG8). In each genetic background, five advanced backcross lines with Pigm (ABLs) and the same genotypes as the respective recurrent parent in the other 13 known R gene loci were developed. Compared with the corresponding recurrent parent, all these ABLs exhibited stronger resistance in seedling inoculation assay using 184 isolates collected from rice growing regions of the lower region of the Yangtze River. With respect to panicle blast resistance, all ABLs reached a high resistance level to blast disease in tests conducted in three consecutive years with the inoculation of seven mixed conidial suspensions collected from different regions of Jiangsu province. In natural field nursery assays, the ABLs showed significantly higher resistance than the recurrent parents. No common change on importantly morphological traits and yield-associated components was found among the ABLs, demonstrating the introduction of Pigm had no tightly linked undesirable effect on rice economically important traits and its associated grain weight reduction effect could be probably offset by others grain weight genes or at least in the background of the aforementioned two varieties. Notably, one rice line with Pigm, designated as Yangnonggeng 3091, had been authorized as a new variety in Jiangsu province in 2021, showing excellent performance on both grain yield and quality, as well as the blast resistance. Together, these results suggest that the Pigm gene has a high breeding value in developing rice varieties with durable and broad-spectrum resistance to blast disease.
The demand for the condition monitoring of induction motors is increasing in various fields, such as industry, transportation, and daily life. Bearing faults are the most common faults, and many fault diagnosis methods have been proposed using artificial pitting as the fault factor in most cases. However, the validity of a fault diagnosis method for other kinds of faults does not seem to be evaluated. Considering onsite scenarios and other possibilities of faults, this paper introduces scratches on the outer raceways of bearings. A study was performed on the detection of several kinds of bearing scratches using a proposed method that was based on an auto-tuning convolutional neural network. The developed approach was also compared with other diagnostic methods for validation. The results showed that the proposed technique provides the possibility of diagnosing several kinds of scratches with acceptable accuracy rates.
The main reservoir hosts of nematodes of the genus Trichinella are wild carnivores, although most human infections are caused by the consumption of pork. This group of zoonotic parasites completes the entire natural life cycle within the host organism. However, there is an important phase of the cycle that has only been highlighted in recent years and which concerns the permanence of the infecting larvae in the striated muscles of the host carcasses waiting to be ingested by a new host. To survive in this unique biological niche, Trichinella spp. larvae have developed an anaerobic metabolism for their survival in rotting carcasses and, for some species, a resistance to freezing for months or years in cold regions. Climate changes with increasingly temperatures and reduction of environmental humidity lower the survival time of larvae in host carcasses. In addition, environmental changes affect the biology and ecology of the main host species, reducing their number and age composition due to natural habitat fragmentation caused by increasing human settlements, extensive monocultures, increasing number of food animals, and reduction of trophic chains and biodiversity. All of these factors lead to a reduction in biological and environmental complexity that is the key to the natural host-parasite balance. In conclusion, Trichinella nematodes can be considered as an indicator of a health natural ecosystem.