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DNA-synthesis providers screen incoming orders by searching the requested sequence against curated hazard lists. We show that this baseline collapses to a 100% false-flag rate when the hazardous sequence comes from a taxonomic family absent from the reference set: under Conformal Risk Control's certified miss-rate constraint, a low-discrimination signal forces the threshold below the entire test-benign mass. We compose three signals derived from a synthesis order's public annotation: $k$-mer Jaccard similarity to known toxins, the trimmed-mean score of a five-LLM judge panel, and cosine similarity to clustered embedding centroids. Fused under a monotone logistic aggregator and calibrated by Conformal Risk Control, the resulting screener certifies $\mathbb{E}[\mathrm{FNR}] \le α+ \mathrm{TV}$, where the additive term is the calibration-to-test distribution shift under family holdout (a certified ceiling of 24-49% across folds). Across ten leave-one-taxonomic-family-out folds at $α=0.05$ on UniProt KW-0800 reviewed toxins, the calibrated screener achieves 0% empirical test miss rate on every fold and 0% test false-flag rate on nine of ten folds. The bound's finite-sample slack $1/(n_

This work investigates how toxin-mediated interactions and directed movements shape the emergence of coherent structures in plant-herbivore systems. The analysis focuses on a two-compartment model enclosing a toxin-dependent functional response and a cross-diffusion term that represents ecologically plausible herbivores' movement towards, or away from, vegetation. Two distinct dynamical regimes arise depending on toxicity strength. Under weak toxicity, the system admits at most one biologically feasible coexistence equilibrium, which may lose stability through a Hopf bifurcation generating small-amplitude temporal oscillations. Under strong toxicity, the nonlinear functional response becomes non-monotonic, allowing for multiple coexistence equilibria and abrupt regime shifts. The influence of cross-diffusion on stability is also examined, identifying the conditions under which Turing instabilities and mixed spatiotemporal patterns occur. Near the corresponding bifurcation thresholds, Stuart-Landau amplitude equations are derived via weakly nonlinear analysis, providing a unified framework for the modulation of oscillatory, stationary, and combined Turing-Hopf modes. Numerical simul

Plants come with sophisticated strategies to survive within a highly competing environment. In addition, they need to resist frequent attacks from a variety of herbivores acting alone, in small groups, or in swarms. Since the amount of energy a plant might invest in defense and reproduction is limited, a complex optimization problem emerges. In a shared habitat, plants fight herbivores by shape and camouflage, by the release of specific toxins, or by attracting predators of herbivores. Furthermore, plants alert their surrounding field by signaling substances in the event of an assault. Transported by air or through a network of roots, signaling substances reach neighbors to trigger their defense. The offsprings of a plant commonly grow within a certain distance to benefit from symbiotic protection. We introduce a grid-based visual simulation software for detailed configuration and subsequent processing of the behavior of the resulting system in time and space. In terms of solution to a computational optimization problem inspired by nature, settings with low energy need and long life able to cope with different patterns of attack can be figured out and analyzed. Applications include

Personality traits, such as boldness and shyness, play a significant role in shaping the survival strategies of animals. Industrial pollution has long posed serious threats to ecosystems and is typically distributed heterogeneously. However, how animals with different personalities respond to spatially heterogeneous pollution remains largely unexplored. In this study, we introduce a prey-taxis model with nonlinear cross-diffusion to examine population dynamics in such environments. The global existence of classical solutions is established by deriving initial bounds through energy estimates and improving solution regularity via heat kernel properties and a bootstrap process. Our findings reveal that behavior, population structure, and spatial distribution are heavily influenced by pollution. Bold individuals maintain a competitive advantage in pollution-free or very low-toxin environments, whereas shy individuals become dominant in regions with low to moderate toxin levels. In highly polluted areas, no populations can survive. The spatial pattern of the population is also closely tied to the distribution of toxins. Grazers tend to move along toxin gradient and exhibit periodic beha

Advances in AI, particularly LLMs, have dramatically shortened drug discovery cycles by up to 40% and improved molecular target identification. However, these innovations also raise dual-use concerns by enabling the design of toxic compounds. Prompting Moremi Bio Agent without the safety guardrails to specifically design novel toxic substances, our study generated 1020 novel toxic proteins and 5,000 toxic small molecules. In-depth computational toxicity assessments revealed that all the proteins scored high in toxicity, with several closely matching known toxins such as ricin, diphtheria toxin, and disintegrin-based snake venom proteins. Some of these novel agents showed similarities with other several known toxic agents including disintegrin eristostatin, metalloproteinase, disintegrin triflavin, snake venom metalloproteinase, corynebacterium ulcerans toxin. Through quantitative risk assessments and scenario analyses, we identify dual-use capabilities in current LLM-enabled biodesign pipelines and propose multi-layered mitigation strategies. The findings from this toxicity assessment challenge claims that large language models (LLMs) are incapable of designing bioweapons. This rei

This paper presents a comprehensive study on a novel multilayer surface plasmon resonance (SPR) biosensor designed for detecting trace-level toxins in liquid samples with exceptional precision and efficiency. Leveraging the Kretschmann configuration, the proposed design integrates advanced two-dimensional materials, including black phosphorus (BP) and transition metal dichalcogenides (TMDs), to significantly enhance the performance metrics of the sensor. Key innovations include the optimization of sensitivity through precise material layering, minimization of full-width at half-maximum (FWHM) to improve signal resolution, and maximization of the figure of merit (FoM) for superior detection accuracy. Numerical simulations are employed to validate the structural and functional enhancements of the biosensor. The results demonstrate improved interaction between the evanescent field and the analyte, enabling detection at trace concentrations with higher specificity. This biosensor is poised to contribute to advancements in biochemical sensing, environmental monitoring, and other critical applications requiring high-sensitivity toxin detection.

Botulinum toxin (Botox) injections are the gold standard for managing facial asymmetry and aesthetic rejuvenation, yet determining the optimal dosage remains largely intuitive, often leading to suboptimal outcomes. We propose a localized latent editing framework that simulates Botulinum Toxin injection effects for injection planning through dose-response modeling. Our key contribution is a Region-Specific Latent Axis Discovery method that learns localized muscle relaxation trajectories in StyleGAN2's latent space, enabling precise control over specific facial regions without global side effects. By correlating these localized latent trajectories with injected toxin units, we learn a predictive dose-response model. We rigorously compare two approaches: direct metric regression versus image-based generative simulation on a clinical dataset of N=360 images from 46 patients. On a hold-out test set, our framework demonstrates moderate-to-strong structural correlations for geometric asymmetry metrics, confirming that the generative model correctly captures the direction of morphological changes. While biological variability limits absolute precision, we introduce a hybrid "Human-in-the-L

Biological systems, with many interacting components, face high-dimensional environmental fluctuations, ranging from diverse nutrient deprivations to toxins, drugs, and physical stresses. Yet, many biological control mechanisms are `simple' -- they restore homeostasis through low-dimensional representations of the system's high-dimensional state. How do low-dimensional controllers maintain homeostasis in high-dimensional systems? We develop an analytically tractable model of integral feedback for complex systems in fluctuating environments. We find that selection for homeostasis leads to the emergence of a soft mode that provides the dimensionality reduction required for the functioning of simple controllers. Our theory predicts that simple controllers that buffer environmental perturbations (e.g., stress response pathways) will also buffer mutational perturbation, an equivalence we test using experimental data across ~5000 strains in the yeast knockout collection. We also predict, counterintuitively, that knocking out a simple controller will \emph{decrease} the dimensionality of the response to environmental change; we outline transcriptomics tests to validate this. Our work sugg

We investigate the dynamics of a discrete phytoplankton-zooplankton model incorporating Holling type~III predation and Holling type~II toxin release. The existence and stability of positive fixed points are analyzed, and it is shown that when two such points, $E_1$ and $E_2$, exist, $E_2$ is always a saddle. A Neimark-Sacker bifurcation at $E_1$ is verified using the normal form method, indicating the emergence of closed invariant curves. This bifurcation implies that phytoplankton and zooplankton populations may exhibit sustained periodic oscillations, which could correspond to natural plankton bloom cycles. The global stability of the boundary equilibrium $(1,0)$ is also established. Numerical simulations are presented to illustrate and confirm the theoretical findings.

Cervical dystonia, a debilitating neurological disorder marked by involuntary muscle contractions and chronic pain, presents significant treatment challenges despite advances in botulinum toxin therapy. While botulinum toxin type B has emerged as one of the leading treatments, comparative efficacy across doses and the influence of demographic factors for personalized medicine remain understudied. This study aimed to: (1) compare the efficacy of different botulinum toxin type B doses using Bayesian methods, (2) evaluate demographic and clinical factors affecting treatment response, and (3) establish a probabilistic framework for personalized cervical dystonia management. We analyzed data from a multicenter randomized controlled trial involving 109 patients assigned to placebo, 5,000 units, or 10,000 units of botulinum toxin type B groups. The primary outcome was the Toronto Western Spasmodic Torticollis Rating Scale measured over 16 weeks. Bayesian hierarchical modeling assessed treatment effects while accounting for patient heterogeneity. Lower botulinum toxin type B doses (5,000 units) showed greater overall Toronto Western Spasmodic Torticollis Rating Scale score reductions (trea

Air pollution, particularly airborne particulate matter (PM), poses a significant threat to public health globally. It is crucial to comprehend the association between PM-associated toxic components and their cellular targets in humans to understand the mechanisms by which air pollution impacts health and to establish causal relationships between air pollution and public health consequences. Although many studies have explored the impact of PM on human health, the understanding of the association between toxins and the associated targets remain limited. Leveraging cutting-edge deep learning technologies, we developed tipFormer (toxin-protein interaction prediction based on transformer), a novel deep-learning tool for identifying toxic components capable of penetrating human cells and instigating pathogenic biological activities and signaling cascades. Experimental results show that tipFormer effectively captures interactions between proteins and toxic components. It incorporates dual pre-trained language models to encode protein sequences and chemicals. It employs a convolutional encoder to assimilate the sequential attributes of proteins and chemicals. It then introduces a learnin

Diarrhetic Shellfish Poisoning (DSP) is a global health threat arising from shellfish contaminated with toxins produced by dinoflagellates. The condition, with its widespread incidence, high morbidity rate, and persistent shellfish toxicity, poses risks to public health and the shellfish industry. High biomass of toxin-producing algae such as DSP are known as Harmful Algal Blooms (HABs). Monitoring and forecasting systems are crucial for mitigating HABs impact. Predicting harmful algal blooms involves a time-series-based problem with a strong historical seasonal component, however, recent anomalies due to changes in meteorological and oceanographic events have been observed. Stream Learning stands out as one of the most promising approaches for addressing time-series-based problems with concept drifts. However, its efficacy in predicting HABs remains unproven and needs to be tested in comparison with Batch Learning. Historical data availability is a critical point in developing predictive systems. In oceanography, the available data collection can have some constrains and limitations, which has led to exploring new tools to obtain more exhaustive time series. In this study, a machi

We study the dynamics of a discrete phytoplankton-zooplankton model with Holling type~II grazing and Holling type~III toxin release. The existence and stability of positive fixed points are analyzed, and conditions for the occurrence of Neimark--Sacker bifurcation are established. We show how feedback control can suppress complex dynamics near bifurcation. Global stability of the boundary equilibrium is also discussed. Numerical simulations confirm the theoretical findings, illustrating the rich behavior of the model under varying parameters

The Shiga toxins comprise a family of related protein toxins secreted by certain types of bacteria. Shigella dysenteriae, some strain of Escherichia coli and other bacterias can express toxins which caused serious complication during the infection. Shiga toxin and the closely related Shiga-like toxins represent a group of very similar cytotoxins that may play an important role in diarrheal disease and hemolytic-uremic syndrome. The outbreaks caused by this toxin raised serious public health crisis and caused economic losses. These toxins have the same biologic activities and according to recent studies also share the same binding receptor, globotriosyl ceramide (Gb3). Rapid detection of food contamination is therefore relevant for the containment of food-borne pathogens. The conventional methods to detect pathogens, such as microbiological and biochemical identification are time-consuming and laborious. The immunological or nucleic acid-based techniques require extensive sample preparation and are not amenable to miniaturization for on-site detection. In the present are necessary of techniques of rapid identification, simple and sensitive which can be employed in the countryside wi

Transcription Factors (TFs) are proteins crucial for regulating gene expression. Effective regulation requires the TFs to rapidly bind to their correct target, enabling the cell to respond efficiently to stimuli such as nutrient availability or the presence of toxins. However, the search process is hindered by slow diffusive movement and the presence of `false' targets --DNA segments that are similar to the true target. In eukaryotic cells, most TFs contain an Intrinsically Disordered Region (IDR), which is commonly assumed to behave as a long, flexible polymeric tail composed of hundreds of amino acids. Recent experimental findings indicate that the IDR of certain TFs plays a pivotal role in the search process. However, the principles underlying the IDR's role remain unclear. Here, we reveal key design principles of the IDR related to TF binding affinity and search time. Our results demonstrate that the IDR significantly enhances both of these aspects. Furthermore, our model shows good agreement with experimental results, and we propose further experiments to validate the model's predictions.

Host-pathogen interactions consist of an attack by the pathogen, frequently a defense by the host and possibly a counter-defense by the pathogen. Here, we present a game-theoretical approach to describing such interactions. We consider a game where the host and pathogen are players and they can choose between the strategies of defense (or counter-defense) and no response. Specifically, they may or may not produce a toxin and an enzyme degrading the toxin, respectively. We consider that the host and pathogen must also incur a cost for toxin or enzyme production. We highlight both the sequential and non-sequential versions of the game and determine the Nash equilibria. Further, we resolve a paradox occurring in that interplay. If the inactivating enzyme is very efficient, producing the toxin becomes useless, leading to the enzyme being no longer required. Then, production of the defense becomes useful again. In game theory, such situations can be described by a generalized matching pennies game. As a novel result, we find under which conditions the defense cycle leads to a steady state or to an oscillation. We obtain, for saturating dose-response kinetics and considering monotonic co

Small open reading frames are understudied as they have been historically excluded from genome annotations. However, evidence for the functional significance of small proteins in various cellular processes accumulates. Proteins with less than 70 residues can also confer resistance to antimicrobial compounds, including intracellularly-acting protein toxins, membrane-acting antimicrobial peptides and various small-molecule antibiotics. Such herein coined Small Antimicrobial Resistance Proteins (SARPs) have emerged on evolutionary timescales or can be enriched from protein libraries using laboratory evolution. Our review consolidates existing knowledge on SARPs and highlights recent advancements in proteomics and genomics that reveal pervasive translation of unannotated genetic regions into small proteins that show features of known SARPs. The potential contribution of small proteins to antimicrobial resistance is awaiting exploration.

Most synthetic microswimmers do not reach the autonomy of their biological counterparts in terms of energy supply and diversity of motion. Here we report the first all-aqueous droplet swimmer powered by self-generated polyelectrolyte gradients, which shows memory-induced chirality while self-solidifying. An aqueous solution of surface tension-lowering polyelectrolytes self-solidifies on the surface of acidic water, during which polyelectrolytes are gradually emitted into the surrounding water and induce linear self-propulsion via spontaneous symmetry breaking. The low diffusion coefficient of the polyelectrolytes leads to long-lived chemical trails which cause memory effects that drive a transition from linear to chiral motion without requiring any imposed symmetry breaking. The droplet swimmer is capable of highly efficient removal (up to 85%) of uranium from aqueous solutions within 90 min, benefiting from self-propulsion and flow-induced mixing. Our results provide a route to fueling self-propelled agents which can autonomously perform chiral motion and collect toxins.