Data-driven discovery of governing equations from time-series data provides a powerful framework for understanding complex biological systems. Library-based approaches that use sparse regression over candidate functions have shown considerable promise, but they face a critical challenge when candidate functions become strongly correlated: numerical ill-conditioning. Poor or restricted sampling, together with particular choices of candidate libraries, can produce strong multicollinearity and numerical instability. In such cases, measurement noise may lead to widely different recovered models, obscuring the true underlying dynamics and hindering accurate system identification. Although sparse regularization promotes parsimonious solutions and can partially mitigate conditioning issues, strong correlations may persist, regularization may bias the recovered models, and the regression problem may remain highly sensitive to small perturbations in the data. We present a systematic analysis of how ill-conditioning affects sparse identification of biological dynamics using benchmark models from systems biology. We show that combinations involving as few as two or three terms can already exhibit strong multicollinearity and extremely large condition numbers. We further show that orthogonal polynomial bases do not consistently resolve ill-conditioning and can perform worse than monomial libraries when the data distribution deviates from the weight function associated with the orthogonal basis. Finally, we demonstrate that when data are sampled from distributions aligned with the appropriate weight functions corresponding to the orthogonal basis, numerical conditioning improves, and orthogonal polynomial bases can yield improved model recovery accuracy across two baseline models.
1 Abstract Humans have long employed directed evolution (DE) to engineer desired biological traits. In this paper, I introduce an algebraic framework that provides a quantitative representation of the general phenotypic traits of asexual populations, enabling the systematic modeling of DE processes. Within this framework, key evolutionary quantities such as the time required for a DE to reach a desired trait or the probability of arriving at a target algebra can be computed or qualitatively analyzed using principles from the evolutionary dynamics of an asexual population. As illustrative examples of trait-representing algebras, I evolve an integer, a two-dimensional vector, a four-dimensional vector with modulo-4 elements, a 2 by 2 matrix, and a cyclic algebra. The generations needed to reach the objective algebra in the DE simulations were consistent with those predicted by the theoretical analysis. Furthermore, I propose a method for mathematically designing evolutionary pathways that minimize the generations needed to reach the desired algebra, offering a key criterion for improving the efficiency of DE. Finally, I discuss how this algebraic approach can be applied in practical experimental setups and outline directions for future research in algebraic modeling of evolution.
The development of accurate and high-throughput tools for cancer biomarker detection is crucial for the diagnosis, monitoring, and treatment of diseases. In this study, we developed a simple and rapid fluorescence-linked immunosorbent assay (FLISA) using fluorescent dye-conjugated antibody fragments against programmed cell death ligand 1 (PDL1) and human epithelial growth factor receptor 2 (HER2). We optimized key steps in the FLISA process, including antigen immobilization, blocking, and antibody reaction, reading the assay time to 3 h—significantly faster compared to the 23 h duration of usual FLISA. The limit of detection for the rapid FLISA in detecting PDL1 was lower than that of FLISA, and the detection of HER2 was similar between the two methods, indicating that the rapid FLISA provides a fast and accurate approach for detecting PDL1 and HER2. This robust platform can be readily adapted for various fluoroimmunoassays targeting other antigens of interest.
Patrick Michael, Robert J. Reid, Robert W. Fitzpatrick
The long-term roles of live plant roots in mitigating acid sulfate soil stresses remain poorly understood. Three studies, each lasting twelve months, were conducted using Melaleuca armillaris and Phragmites australis. In the first study, alkaline sandy loam soil was mixed into the sulfuric soil to increase the pH to 6.7, and Melaleuca seedlings were planted. In the second and third studies, M. armillaris and P. australis were planted in sulfuric and sulfidic soils and maintained at 75% water-holding capacity and flooded soil conditions. All the studies were set using 300 mm stormwater tubes with sealed bottom ends. The treatments were replicated four times, set up under a glasshouse in a completely randomized design, and harvested after 12 months. The pH and root biomass were measured from the surface, middle, and deep profiles. Results showed that the neutralization obtained by mixing alkaline sandy loam soil with sulfuric soil was stable but deteriorated due to plant root penetration. In the sulfuric soil material (pH <4), M. armillaris produced more roots at the surface than in the deep soil under circumneutral pH and aerobic soil conditions. In sulfidic soil material (pH >4), more roots were produced in the deeper soils. In the sulfuric and sulfidic soil materials, P. australis produced more roots at the surface than at the deep under pH >4 and aerobic conditions. Under anaerobic conditions with a pH >4, root distribution was even. Our findings suggest that common terrestrial and aquatic plants maintain a characteristic distribution of roots to mitigate the stresses of acid sulfate soils.
Juliane Mailly, Louise Riotte-Lambert, Mathieu Lihoreau
Accurate prediction of pollination processes is a key challenge for sustainable food production and the conservation of natural ecosystems. For many plants, pollen dispersal is mediated by the foraging movements of nectarivore animals. While most current models of pollination ecology assume random pollen movements, studies in animal behaviour show how pollinating insects, birds and bats rely on sensory cues, learning and memory to visit flowers, thereby producing complex movement patterns. Building upon a brief review of pollination and movement models, we argue that we need to better consider pollinators’ cognition to improve predictions of animal-mediated pollination across all spatial scales, from individual flowers, to plants, habitat patches and landscapes. We propose a practical roadmap for the integration of behavioural models into pollination models and discuss how this synthesis can refine predictions regarding plant mating patterns and fitness. Such crosstalk between animal behaviour and plant ecology research will provide powerful mechanistic tools to predict and act on pollination services in the context of a looming crisis.
Robert Mzungu Runya, Chris McGonigle, Rory Quinn
et al.
ABSTRACT Understanding the spatial dynamics of harbour porpoise (Phocoena phocoena) is crucial for effective conservation and management. The study presents a multidisciplinary approach to modelling and analysing the site occurrence and habitat use of Phocoena phocoena within the Skerries and Causeway Special Area of Conservation (SAC), identifying areas where they were seen surfacing and/or spending the most time. Using data derived from multibeam echosounders (MBES), particle size analysis of sediments, hydrodynamic modelling, and theodolite tracking observations, the study examines the influence of local hydrodynamics and environmental conditions on the spatial distribution of harbour porpoises. Kernel density analysis of 451 porpoise sightings over an 11‐day survey demonstrated that dense clusters and higher aggregations occurred within ~500 m of the shoreline. Generalised Additive Models (GAMs) identified slope, aspect, backscatter intensity and sediment grain size as the most significant environmental predictors, accounting for 47.6% of the deviance in harbour porpoise distribution. Porpoises' occurrence was particularly spatially coincident with coarser sediments (4.25–5 mm), and their distribution was highly concentrated around headlands, shoreline and within a 3‐h window before and after high water. Overall, these findings highlight the dynamic nature of harbour porpoises' use of habitat in space and time, with models predicting a high probability of porpoise encounters (> 0.6) nearshore, particularly in headland areas characterised by local flow acceleration and coarser seabeds. The study presents a robust workflow for developing a porpoise‐specific monitoring program. By leveraging multidisciplinary methodological approaches, the study provides a scientific basis for refining marine conservation measures, delivering long‐term protection for harbour porpoise habitats under existing legal and management frameworks both within and beyond the SAC boundaries.
The ocean vast unexplored regions and diverse soft-bodied marine organisms have spurred interest in bio-inspired underwater soft robotics. Recent advances have enabled new capabilities in underwater movement, sensing, and interaction. However, these efforts are largely unidirectional, with biology guiding robotics while insights from robotics rarely feed back into biology. Here we propose a holistic, bidirectional framework that integrates biological principles, robotic implementation, and biological validation. We show that soft robots can serve as experimental tools to probe biological functions and even test evolutionary hypotheses. Their inherent compliance also allows them to outperform rigid systems in unstructured environments, supporting applications in marine exploration, manipulation, and medicine. Looking forward, we introduce bio-universal-inspired robotics, a paradigm that transcends species-specific mimicry by identifying convergent principles across species to inspire more adaptable designs. Despite rapid progress, challenges persist in material robustness, actuation efficiency, autonomy, and intelligence. By uniting biology and engineering, soft robots can advance ocean exploration and deepen scientific discovery.
Aqueous metabolites in terrestrial subsurface environments provide critical analog frameworks for assessing the habitability of Martian subsurface ice. On Earth, they play critical roles in sustaining microbial life within soils, permafrost, and groundwater environments and their availability shape microbial community compositions, activity, and adaptability to changes in environmental conditions, enabling communities to persist over millennial timescales. The counterpart to aqueous-soluble organics is the insoluble organic matter pool that makes up the largest portion of organic matter in natural samples and includes most types of organic signatures indicative of biological processes. Employing a range of sample preparation, molecular separation, detection, and imaging techniques enables the characterization of both labile (i.e., soluble and reactive) and recalcitrant (i.e., insoluble, non-reactive; include macromolecules) organic pools. Multiple orthogonal analytical modalities strengthen interpretations of signatures that we associate with biology as we know it and don't know it, by constraining possible abiotic sources, validating measurements across distinct techniques, and ensuring flexibility to interrogate diverse organic chemistries encountered in Martian subsurface environments. This holistic triage approach aligns with the priorities articulated in the Mars Exploration Program Analysis Group's Search for Life -Science Analysis Group (SFL-SAG) Charter for a medium-class Mars mission focused on extant life detection.
Pawel Sledzinski, Mateusz Nowaczyk, Marianna Iga Smielowska
et al.
Abstract Background The expansion of CAG/CTG repeats in functionally unrelated genes is a causative factor in many inherited neurodegenerative disorders, including Huntington’s disease (HD), spinocerebellar ataxias (SCAs), and myotonic dystrophy type 1 (DM1). Despite many years of research, the mechanism responsible for repeat instability is unknown, and recent findings indicate the key role of DNA repair in this process. The repair of DSBs induced by genome editing tools results in the shortening of long CAG/CTG repeats in yeast models. Understanding this mechanism is the first step in developing a therapeutic strategy based on the controlled shortening of repeats. The aim of this study was to characterize Cas9-induced DSB repair products at the endogenous HTT locus in human cells and to identify factors affecting the formation of specific types of sequences. Results The location of the cleavage site and the surrounding sequence influence the outcome of DNA repair. DSBs within CAG repeats result in shortening of the repeats in frame in ~ 90% of products. The mechanism of this contraction involves MRE11-CTIP and RAD51 activity and DNA end resection. We demonstrated that a DSB located upstream of CAG repeats induces polymerase theta-mediated end joining, resulting in deletion of the entire CAG tract. Furthermore, using proteomic analysis, we identified novel factors that may be involved in CAG sequence repair. Conclusions Our study provides new insights into the complex mechanisms of CRISPR/Cas9-induced shortening of CAG repeats in human cells.
Abstract Nanodrugs capable of aggregating in the tumor microenvironment (TME) have demonstrated great efficiency in improving the therapeutic outcome. Among various approaches, the strategy utilizing electrostatic interaction as a driving force to achieve intratumor aggregation of nanodrugs has attracted great attention. However, the great difference between the two nanodrugs with varied physicochemical properties makes their synchronous transport in blood circulation and equal‐opportunity tumor uptake impossible, which significantly detracts from the beneficial effects of nanodrug aggregation inside tumors. We herein propose a new strategy to construct a pair of extremely similar nanodrugs, referred to as “twins‐like nanodrugs (TLNs)”, which have identical physicochemical properties including the same morphology, size, and electroneutrality to render them the same blood circulation time and tumor entrance. The 1:1 mixture of TLNs (TLNs‐Mix) intravenously injected into a mouse model efficiently accumulates in tumor sites and then transfers to oppositely charged nanodrugs for electrostatic interaction‐driven coalescence via responding to matrix metalloproteinase‐2 (MMP‐2) enriched in tumor. In addition to enhanced tumor retention, the thus‐formed micron‐sized aggregates show high echo intensity essential for ultrasound imaging as well as ultrasound‐triggered penetrative drug delivery. Owing to their distinctive features, the TLNs‐Mix carrying sonosensitizer, immune adjuvant, and ultrasound contrast agent exert potent sonodynamic immunotherapy against hypovascular hepatoma, demonstrating their great potential in treating solid malignancies.
In response to the challenge of single navigation methods failing to meet the high precision requirements for unmanned aerial vehicle (UAV) navigation in complex environments, a novel algorithm that integrates Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) navigation information is proposed to enhance the positioning accuracy and robustness of UAV navigation systems. First, the fundamental principles of Kalman filtering and its application in navigation are introduced. Second, the basic principles of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks and their applications in the navigation domain are elaborated. Subsequently, an algorithm based on a CNN and LSTM-assisted Kalman filtering fusion navigation is proposed. Finally, the feasibility and effectiveness of the proposed algorithm are validated through experiments. Experimental results demonstrate that the Kalman filtering fusion navigation algorithm assisted by a CNN and LSTM significantly improves the positioning accuracy and robustness of UAV navigation systems in highly interfered complex environments.
Elsa Lawrence, Adham El-Shazly, Srijit Seal
et al.
Modern life sciences research is increasingly relying on artificial intelligence approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific research, yet it is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research - protein structure prediction and single cell RNA-sequencing - we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems.
Guided by the Einstein equivalence principle that identifies the phenomenon of gravitation as a manifestation of the dynamics of spacetime in contrast to a localizable force, we review and explore its consequences on formulating a theory of gravity. The resulting space of metric theories of gravity may address open conceptual and observational puzzles through a wealth of effects beyond general relativity, whose traces can be searched for within today's and tomorrow's gravitational testing grounds. Above all, we offer a generic metric theory generalization of Isaacson's approach to the leading-order field equations of physical perturbations with a well-defined notion of energy-momentum carried by the gravitational waves. Within this framework, we identify the backreaction of the Isaacson energy-momentum flux onto the background spacetime with the displacement memory effect that induces a permanent distortion of space after the passage of a gravitational wave. This effect is a well-known prediction of GR whose dominant contribution captures its inherent non-linear nature, manifest in the ability of gravity to gravitate. However, the novel interpretation of memory as naturally arising within the Isaacson approach to gravitational waves comes with two main advantages. Firstly, it allows for a unified understanding of both the null and the ordinary memory effect, which are respectively sourced by unbound energy fluxes that do and do not reach asymptotic null infinity. Secondly, and most importantly, this approach allows for a consistent derivation of the memory formula for a large class of metric theories with considerable lessons to be learned for upcoming future measurements of the memory effect.
Hemichordates are close relatives of chordates. Their nervous system patterning is chordate-like, but their neural architecture remains unexplored. A new study in PLOS Biology reveals an unexpected neuroanatomical complexity in these animals, also informing chordate origins.
Connexins are important proteins involved in cell-to-cell communication and cytodifferentiation during renewal and cornification of the multilayered epithelia. So far, there is a lack of reports on this subject in birds’ structurally different ortho- and parakeratinized epithelium of the tongue. The study aims to describe the distribution and expression profiles of the α-connexins (Cx40 and 43) and β-connexins (Cx26, 30, and 31) in those epithelia in duck, goose, and domestic turkey. Research revealed the presence of the mentioned connexins and the occurrence of interspecies differences. Connexins form gap junctions in the cell membrane or are in the cytoplasm of keratinocytes. Differences in connexin expression were noted between the basal and intermediate layers, which may determine the proliferation of keratinocytes. Cx40, 43, and Cx30 in the gap junction of the keratinocytes of the intermediate layer are related to the synchronization of the cornification process. Because of the exfoliation of cornified plaques, a lack of connexins was observed in the cornified layer of orthokeratinized epithelium. However, in parakeratinized epithelium, connexins were present in the cell membrane of keratinocytes and thus maintained cellular integrity in gradually desquamating cells. The current studies will be useful in further comparative analyses of normal and pathological epithelia of the oral cavity in birds.
The physiological functions of endothelial cells control vascular tone, permeability, inflammation, and angiogenesis, which significantly help to maintain a healthy vascular system. Several cardiovascular diseases are characterized by endothelial cell activation or dysfunction triggered by external stimuli such as disturbed flow, hypoxia, growth factors, and cytokines in response to high levels of low-density lipoprotein and cholesterol, hypertension, diabetes, aging, drugs, and smoking. Increasing evidence suggests that uncontrolled proinflammatory signaling and further alteration in endothelial cell phenotypes such as barrier disruption, increased permeability, endothelial to mesenchymal transition (EndMT), and metabolic reprogramming further induce vascular diseases, and multiple studies are focusing on finding the pathways and mechanisms involved in it. This review highlights the main proinflammatory stimuli and their effects on endothelial cell function. In order to provide a rational direction for future research, we also compiled the most recent data regarding the impact of endothelial cell dysfunction on vascular diseases and potential targets that impede the pathogenic process.
Diffusion models have shown remarkable success in visual synthesis, but have also raised concerns about potential abuse for malicious purposes. In this paper, we seek to build a detector for telling apart real images from diffusion-generated images. We find that existing detectors struggle to detect images generated by diffusion models, even if we include generated images from a specific diffusion model in their training data. To address this issue, we propose a novel image representation called DIffusion Reconstruction Error (DIRE), which measures the error between an input image and its reconstruction counterpart by a pre-trained diffusion model. We observe that diffusion-generated images can be approximately reconstructed by a diffusion model while real images cannot. It provides a hint that DIRE can serve as a bridge to distinguish generated and real images. DIRE provides an effective way to detect images generated by most diffusion models, and it is general for detecting generated images from unseen diffusion models and robust to various perturbations. Furthermore, we establish a comprehensive diffusion-generated benchmark including images generated by eight diffusion models to evaluate the performance of diffusion-generated image detectors. Extensive experiments on our collected benchmark demonstrate that DIRE exhibits superiority over previous generated-image detectors. The code and dataset are available at https://github.com/ZhendongWang6/DIRE.
Background: Left ventricular noncompaction (LVNC) is a rare cardiomyopathy, long QT syndrome (LQTS) is a rare ion channel disease, and simultaneous occurrence of both is even rarer. Further clinical reports and studies are needed to identify the association between LVNC and LQTS and the underlying mechanism.Methods and Results: A 26-year-old primigravida was referred at 25 weeks gestation for prenatal echocardiography due to fetal bradycardia detected during the routine ultrasound examination. The echocardiographic findings were consistent with biventricular noncompaction cardiomyopathy (BVNC) with pulmonary stenosis and suspected LQTS. After detailed counseling, the couple decided to terminate the pregnancy, and subsequent postmortem examination confirmed BVNC and pulmonary stenosis. Then, A trio (fetus and the parents) whole-exome sequencing (WES) and copy number variation sequencing (CNV-seq) were performed. CNV-seq identified no aneuploidy or pathogenic CNV. A de novo missense variant in KCNH2 (NM_000238.3:c.1847A > G,p.Tyr616Cys) was identified by WES. This KCNH2 missense mutation was classified as pathogenic according to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology variant interpretation guidelines.Conclusion: We report the first prenatal case of KCNH2 mutation presenting with LVNC combined with bradycardia and second-degree 2:1 atrioventricular block. Importantly, this case reminds clinicians to systematically search ion channel gene mutations in patients with LVNC and arrhythmia.