Julyan H. E. Cartwright, Charles S. Cockell, Julie G. Cosmidis
et al.
Both abiotic self-organization and biological mechanisms have been put forward as the origin of a number of geological patterns. It is important to comprehend the formation mechanisms of such structures both to understand geological self-organization and in order to differentiate them from biological patterns -- fossils and bio-influenced structures -- seen in geological systems. Being able to distinguish the traces of biological activity from geological self-organization is fundamental both for understanding the origin of life on Earth and for the search for life beyond Earth.
Collective systems that self-organise to maximise the group's ability to collect and distribute information can be successful in environments with high spatial and temporal variation. Such organisations are abundant in nature, as sharing information is a key benefit of many biological collective systems, and have been influential in the design of many artificial collectives such as swarm robotics. Understanding how these systems may be spatially distributed to optimise their collective potential is therefore of importance in both ecology and in collective systems design. Here, we develop a mathematical model which uses an optimisation framework to determine the higher-order spatial structure of a collective that optimises group-level knowledge transfer. The domain of the objective function is a set of weighted simplicial sets, which can fully represent the spatial structure from a topological perspective. By varying the parameters within the objective function and the constraints, we determine how the optimal spatial structure may vary when individuals differ in their information gathering ability and how this variation differs in the context of resource constraints. Our key findings are that the amount of resources in the environment can lead to specific subgroup sizes being optimal for the group as a whole when individuals are homogeneous in their information gathering abilities. Further, when there is variation in information gathering abilities, our model implies that the sharing of space between smaller subgroups of the population, rather than the whole population, is optimal for collective knowledge sharing. Our results have applications across diverse contexts from behavioural ecology to bio-inspired collective systems design.
Md Anisur Rahman, Md Asif Hasan Khan, Tuan Mai
et al.
Bioprinting technology has advanced significantly in the fabrication of tissue-like constructs with complex geometries for regenerative medicine. However, maintaining the structural integrity of bioprinted materials remains a major challenge, primarily due to the frequent and unexpected formation of hidden defects. Traditional defect detection methods often require physical contact that may not be suitable for hydrogel-based biomaterials due to their inherently soft nature, making non-invasive and straightforward structural evaluation necessary in this field. To advance the state of the art, this study presents a novel non-contact method for non-destructively detecting structural defects in bioprinted constructs using video-based vibration analysis. Ear-shaped constructs were fabricated using a bioink composed of sodium alginate and \k{appa}-carrageenan using extrusion-based bioprinting. To simulate printing defects, controlled geometric, interlayer, and pressure-induced defects were systematically introduced into the samples. The dynamic response of each structure was recorded using a high-speed camera and analyzed via phase-based motion estimation techniques. Experimental results demonstrate that all defective samples exhibit consistent changes in the dynamic characteristics compared to baseline samples, with increasingly pronounced deviation observed as defect severity increases, which reflect changes in effective stiffness and mass distribution induced by internal anomalies, even when such defects are not detectable through surface inspection. The experimental trends were also validated through finite element simulations. Overall, this work demonstrates that video-based vibrometry is a powerful approach for assessing the quality of bioprinted constructs, offering a practical pathway toward robust structural health monitoring in next-generation bio-additive manufacturing workflows.
Roman Bushuiev, Anton Bushuiev, Niek F. de Jonge
et al.
The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://github.com/pluskal-lab/MassSpecGym.
The Hypothalamic-Pituitary-Adrenal (HPA) axis is a major neuroendocrine system, and its dysregulation is implicated in various diseases. This system also presents interesting mathematical challenges for modeling. We consider a nonlinear delay differential equation model and calculate pseudospectra of three different linearizations: a time-dependent Jacobian, linearization around the limit cycle, and dynamic mode decomposition (DMD) analysis of Koopman operators (global linearization). The time-dependent Jacobian provided insight into experimental phenomena, explaining why rats respond differently to perturbations during corticosterone secretion's upward versus downward slopes. We developed new mathematical techniques for the other two linearizations to calculate pseudospectra on Banach spaces and apply DMD to delay differential equations, respectively. These methods helped establish local and global limit cycle stability and study transients. Additionally, we discuss using pseudospectra to substantiate the model in experimental contexts and establish bio-variability via data-driven methods. This work is the first to utilize pseudospectra to explore the HPA axis.
This study introduces an artificial neural network (ANN) for image classification task, inspired by the aversive olfactory learning circuits of the nematode Caenorhabditis elegans (C. elegans). Despite the remarkable performance of ANNs in a variety of tasks, they face challenges such as excessive parameterization, high training costs and limited generalization capabilities. C. elegans, with its simple nervous system comprising only 302 neurons, serves as a paradigm in neurobiological research and is capable of complex behaviors including learning. This research identifies key neural circuits associated with aversive olfactory learning in C. elegans through behavioral experiments and high-throughput gene sequencing, translating them into an image classification ANN architecture. Additionally, two other image classification ANNs with distinct architectures were constructed for comparative performance analysis to highlight the advantages of bio-inspired design. The results indicate that the ANN inspired by the aversive olfactory learning circuits of C. elegans achieves higher accuracy, better consistency and faster convergence rates in image classification task, especially when tackling more complex classification challenges. This study not only showcases the potential of bio-inspired design in enhancing ANN capabilities but also provides a novel perspective and methodology for future ANN design.
Representational straightening refers to a decrease in curvature of visual feature representations of a sequence of frames taken from natural movies. Prior work established straightening in neural representations of the primate primary visual cortex (V1) and perceptual straightening in human behavior as a hallmark of biological vision in contrast to artificial feedforward neural networks which did not demonstrate this phenomenon as they were not explicitly optimized to produce temporally predictable movie representations. Here, we show robustness to noise in the input image can produce representational straightening in feedforward neural networks. Both adversarial training (AT) and base classifiers for Random Smoothing (RS) induced remarkably straightened feature codes. Demonstrating their utility within the domain of natural movies, these codes could be inverted to generate intervening movie frames by linear interpolation in the feature space even though they were not trained on these trajectories. Demonstrating their biological utility, we found that AT and RS training improved predictions of neural data in primate V1 over baseline models providing a parsimonious, bio-plausible mechanism -- noise in the sensory input stages -- for generating representations in early visual cortex. Finally, we compared the geometric properties of frame representations in these networks to better understand how they produced representations that mimicked the straightening phenomenon from biology. Overall, this work elucidating emergent properties of robust neural networks demonstrates that it is not necessary to utilize predictive objectives or train directly on natural movie statistics to achieve models supporting straightened movie representations similar to human perception that also predict V1 neural responses.
Eleonora Alfinito, Mariangela Ciccarese, Giuseppe Maruccio
et al.
The growing interest in bio-inspired materials is driven by the need for increasingly targeted and efficient devices that also have a low ecological impact. These devices often use specially developed materials (e.g., polymers, aptamers, monoclonal antibodies) capable of carrying out the process of recognizing and capturing a specific target in a similar way to biomaterials of natural origin. In this article, we present two case studies, in which the target is a biomolecule of medical interest, in particular, α-thrombin and cytokine IL-6. In these examples, different biomaterials are compared to establish, with a theoretical-computational procedure known as proteotronics, which of them has the greatest potential for use in a biodevice.
This report describes the purification of an acute phase reactant from acute phase rabbit serum, which endows normal serum with the properties of acute phase serum, insofar as LPS is concerned. The acute phase reactant is referred to as LPS-binding protein, or LBP. LBP was purified approximately 2,000-fold by chromatography of acute phase serum on Bio-Rex 70 and Mono-Q resins. The resulting preparation consisted of two glycoproteins having molecular weights of 60,500 and 58,000; the two were obtained in a variable ratio, usually near 10:1, respectively. After separation by SDS-PAGE, the N-terminal 36 amino acid sequences of the two proteins were identical. From the N-terminal sequence, as well as other properties of LBP, LBP appears to be unrelated to any known acute phase reactants. The direct interaction of LPS and LBP was inferred from two types of evidence: first, immunoprecipitation of [3H]LPS from APRS by anti-LBP sera; and second, by the 125I-labeling of LBP when APRS-containing 125I-labeled 2-(p- azidosalicylamido)ethyl 1,3'-dithiopropionyl-LPS was photolysed. The data presented here support the concept that the 60-kD glycoprotein we have termed LBP is a newly recognized acute phase reactant that may modulate the biochemical and biologic properties of LPS in vivo.
Mohammad Keimasi, Kowsar Salehifard, M. Shahidi
et al.
Memory impairment is one of the main complications of Alzheimer’s disease (AD). This condition can be induced by hyper-stimulation of N-Methyl-D-aspartate receptors (NMDARs) of glutamate in the hippocampus, which ends up to pyramidal neurons determination. The release of neurotransmitters relies on voltage-gated calcium channels (VGCCs) such as P/Q-types. Omega-lycotoxin-Gsp2671e (OLG1e) is a P/Q-type VGCC modulator with high affinity and selectivity. This bio-active small protein was purified and identified from the Lycosa praegrandis venom. The effect of this state-dependent low molecular weight P/Q-type calcium modulator on rats was investigated via glutamate-induced excitotoxicity by N-Methyl-D-aspartate. Also, Electrophysiological amplitude of field excitatory postsynaptic potentials (fEPSPs) in the input–output and Long-term potentiation (LTP) curves were recorded in mossy fiber and the amount of synaptophysin (SYN), synaptosomal-associated protein, 25 kDa (SNAP-25), and synaptotagmin 1(SYT1) genes expression were measured using Real-time PCR technique for synaptic quantification. The outcomes of the current study suggest that OLG1e as a P/Q-type VGCC modulator has an ameliorative effect on excitotoxicity-induced memory defects and prevents the impairment of pyramidal neurons in the rat hippocampus.
High Q-factor resonance holds great promise for bio-chemical sensing and enhanced light–matter interaction. However, terahertz (THz) magnetic resonances usually demonstrate low Q-factors, resulting in huge energy radiation loss particularly in high frequency bands. Here, we show that high Q-factor magnetic dipole resonance at THz frequencies can be achieved by exploiting the coherent Fano interactions with strong field enhancements in an array composed of single metallic split-ring resonators, working at Wood–Rayleigh anomalies. It can give rise to ultrahigh Q-factor beyond 104 in the THz regime. Experimentally, the measured Q-factor of dominant magnetic dipole resonance can achieve no less than a level of ∼261 by Lorentzian fitting to the experimental data. In addition, a high Q-factor of the fundamental-order magnetic dipole resonance is demonstrated beyond 30. High- Q magnetic dipole resonance is closely associated with ultralow-damping and negative permeability in the THz band. The measurements of magnetic dipole resonances are in good agreement with the theoretical analyses. Our scheme suggests a feasible route to suppress radiative loss for enhanced THz field-matter interaction.
The present study describes a SYBR Green real-time quantitative (q) PCR assay to detect Erwinia pyrifoliae in plants. E. pyrifoliae, first described in South Korea, is a phytopathogenic bacterial species in the genus Erwinia. In particular, specific detection, quantitation, and identification of E. pyrifoliae are still challenging, as symptoms resulting from its colonization of Asian pear blossoms are very similar to those caused by Erwinia amylovora. E. pyrifoliae has biochemical, phenotypic, and genetic properties similar to those of E. amylovora. Moreover, other Erwinia species, including Erwinia tasmaniensis and Erwinia billingiae, are also detected by currently available molecular methods and with traditional methods as well. Therefore, in this study, previously published genome sequences of the genera Erwinia and Pantoea were compared to exploit species-specific genes for use as improved qPCR targets to detect E. pyrifoliae. In silico analyses of the selected gene and designed primer sequences, in conjunction with bio-SYBR Green real-time qPCR, confirmed the robustness of this newly developed assay. Consequently, the bio-SYBR Green real-time qPCR-based protocols developed here can be used for rapid and specific detection of E. pyrifoliae. They will potentially simplify and facilitate diagnosis and monitoring of this pathogen, and guide plant disease management.
In this article, a novel optical Bio-microelectromechanical system (MEMS) sensing platform is proposed based on a tunable laser and its lasing wavelength to detect the biomolecules and measure their quantities. The present biosensor consists of a BioMEMS cantilever and a proposed external cavity tunable laser. While the target samples (i.e., DNA, mRNA, or protein) are exposed to the cantilever surface, target-analyte bindings are happened. This can induce a surface stress on the MEMS cantilever and results in its bending due to the surface stress difference in each side of the cantilever. Thus, the gap size between the laser cavity and the gain medium is changed which can be measured by the wavelength variations of the proposed tunable laser source. Consequently, by analyzing the output response, one can detect the amount of target biomolecules in the sample and assign a level of contamination, infection, or bioparticles, caused by the specific disease. Various parameters of the proposed device are designed by numerical and analytical approaches. Furthermore, functional characteristics of the present BioMEMS sensor are obtained as follows: mechanical sensitivity of $1.8~\mu \text{m}$ /Nm−1, optical sensitivity of 422 nm/RIU, Q-factor of 610, and resonant frequency of 6.43 kHz. The obtained functional characteristics of the proposed device show that the present optical BioMEMS sensor can be appealing for highly sensitive diagnoses of various types of diseases and their progress level.
Soil management and cultivar selection are two strategies to reduce the accumulation risk of heavy metals in crops. However, it is still an open question which of these two strategies is more efficient for the safe utilization of contaminated soil. In this study, the available bio-concentration factors (aBCF) of arsenic (As) and cadmium (Cd) among 39 maize cultivars were determined through a field experiment. The effect of soil management was mimicked by choosing diverse sampling sites having different soil available contents of As and Cd. The aBCF of As and Cd in grain ranged from 0.02 to 0.13 and 1.17 to 42.2, respectively. The accumulation ability of As and Cd was classified among different maize cultivars. Soil pH and total As controlled the level of available As in soils, while soil pH dominated available Cd in soil. A soil pH of 6.5 was recommended to simultaneously minimize soil available As and Cd by managing soil conditions. The quantitative effects of cultivar and soil management on grain As and Cd were expressed as Q [Grain As] = 0.746Q [Cultivar]-0.126Q [pH]+0.276Q [Asavailable] (R2 = 0.648, P = 1.00 × 10-37) and Q [Grain Cd] = 0.913Q [Cultivar]-0.192Q [pH]+0.071Q [SOC] (R2 = 0.782, P = 1.00 × 10-37), respectively. Cultivar selection contributed stronger than soil management to decrease the As and Cd levels in maize grains. A feasible method to seek for a more efficient strategy was proposed for the safe utilization of contaminated soil.
While recent work has established divergence as a key framework for understanding evenness, there is currently no research exploring how the families of measures within the divergence-based framework relate to each other. This paper uses geometry to show that, holding order and richness constant, the families of divergence-based evenness measures nest. This property allows them to be ranked based on their reactivity to changes in relatively even assemblages or changes in relatively uneven ones. We establish this ranking and explore how the distance-based measures relate to it for both order q=2 and q=1. We also derive a new family of distance-based measures that captures the angular distance between the vector of relative abundances and a perfectly even vector and is highly reactive to changes in even assemblages. Finally, we show that if we only require evenness to be a divergence, then any smooth, monotonically increasing function of diversity can be made into an evenness measure. A deeper understanding of how to measure evenness will require empirical or theoretical research that uncovers which kind of divergence best reflects the underlying concept.