Abstract Aim Water and fishery resources are essential for human life and regional economies. However, marine ecosystems face increasing pressures from human activities such as urbanization, agriculture, and climate change. Although the effects of rising sea temperatures on fish growth have been well documented [1], the impacts of chemical pollutants, including pesticides and heavy metals, remain less understood. This study aimed to identify environmental pollutants and oceanographic factors influencing Atlantic cod (Gadus morhua) growth using interpretable machine-learning methods. Methods Comprehensive datasets of environmental pollutants and oceanographic parameters from the North Atlantic were compiled. Random forest regression combined with SHAP (SHapley Additive exPlanations) was used to quantify variable importance. Results Zinc (Zn), lead (Pb), and chromium (Cr) exhibited significant negative contributions to predicted body length, suggesting potential growth inhibition associated with these metals. In contrast, mercury (Hg) showed a positive association with body size, suggesting bioaccumulation consistent with previous findings. Furthermore, dissolved oxygen, salinity, and temperature were also identified as important predictors. Conclusion These findings indicate that both chemical pollutants and oceanographic parameters influence cod growth. Recognizing the importance of these two qualitatively different types of environmental factors can contribute to more comprehensive approaches in ecosystem-based fisheries management and in the environmental risk assessment and management of marine pollutants. References [1] Pörtner H.O., Knust R. ‘Oxygen- and capacity-limited thermal tolerance: Linking ecology and physiology.’ Annual Review of Physiology 2010; 72:61–90.

Abstract Background Vision language models (VLMs) are increasingly integrated into medical workflows for diagnostic support and clinical decision-making. While recent studies have demonstrated susceptibility of proprietary VLMs to prompt injection attacks in medical contexts [1], the security landscape of domain-specific medical VLMs remains largely unexplored. This study comprehensively evaluates the vulnerability of multiple VLMs to both prompt injection and adversarial perturbation attacks [2], investigating white-box attacks on MedGemma and black-box transfer attacks across medical-domain and proprietary models. Methods We conducted systematic vulnerability assessment using medical images with histologically confirmed malignant lesions spanning multiple modalities: CT, MRI, ultrasound, pathology, endoscopy, and dermatology (n = 18 cases, 3 per modality). For prompt injection, we embedded malicious instructions within text prompts and visual elements. For adversarial perturbations, we used Projected Gradient Descent and optimization-based methods. White-box attacks utilized full model access to MedGemma, while black-box attacks employed transfer-based methods using surrogate models (OpenCLIP, BiomedCLIP, BLIP). Results MedGemma achieved the lowest prompt injection vulnerability (38% ASR), followed by Claude 4 Sonnet (48%), GPT-5 (57%), and Claude 4.1 Opus (69%), suggesting domain-specific medical training enhances resistance. For adversarial perturbations, white-box attacks on MedGemma exceeded 80% ASR. Black-box transfer attacks showed varying vulnerability: GPT-5 (44%), MedGemma (37%), Claude 4.1 Opus (17%), and Claude 4 Sonnet (6%). Vulnerability rankings differed notably between attack modalities. Conclusions This study provides the first comparative security assessment across medical-domain and proprietary VLMs. Results reveal complex vulnerability patterns with no single model providing universal robustness across different attack vectors. These findings emphasize that robust medical AI security requires comprehensive, multi-layered defenses targeting both text-based and image-based attack vectors, with model-specific threat considerations for medical applications. References 1. Clusmann J. et al. ‘Prompt injection attacks on vision language models in oncology.’ Nature Communications 2025;16:1239. 2. Hirano H., Minagi A. and Takemoto K. ‘Universal adversarial attacks on deep neural networks for medical image classification.’ BMC Medical Imaging 2021; 21:9.

BACKGROUND: Co-occurrence networks-ecological associations between sampled populations of microbial communities inferred from taxonomic composition data obtained from high-throughput sequencing techniques-are widely used in microbial ecology. Several co-occurrence network methods have been proposed. Co-occurrence network methods only infer ecological associations and are often used to discuss species interactions. However, validity of this application of co-occurrence network methods is currently debated. In particular, they simply evaluate using parametric statistical models, even though microbial compositions are determined through population dynamics. RESULTS: We comprehensively evaluated the validity of common methods for inferring microbial ecological networks through realistic simulations. We evaluated how correctly nine widely used methods describe interaction patterns in ecological communities. Contrary to previous studies, the performance of the co-occurrence network methods on compositional data was almost equal to or less than that of classical methods (e.g., Pearson's correlation). The methods described the interaction patterns in dense and/or heterogeneous networks rather inadequately. Co-occurrence network performance also depended upon interaction types; specifically, the interaction patterns in competitive communities were relatively accurately predicted while those in predator-prey (parasitic) communities were relatively inadequately predicted. CONCLUSIONS: Our findings indicated that co-occurrence network approaches may be insufficient in interpreting species interactions in microbiome studies. However, the results do not diminish the importance of these approaches. Rather, they highlight the need for further careful evaluation of the validity of these much-used methods and the development of more suitable methods for inferring microbial ecological networks.

BACKGROUND: Host-pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. RESULTS: We re-evaluated the importance of the reverse ecology method for evaluating host-pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data. Our data analyses showed that host-pathogen interactions were more strongly influenced by genome size, primary network parameters (e.g., number of edges), oxygen requirement, and phylogeny than the reserve ecology-based measures. CONCLUSION: These results indicate the limitations of the reverse ecology method; however, they do not discount the importance of adopting reverse ecology approaches altogether. Rather, we highlight the need for developing more suitable methods for inferring host-pathogen interactions and conducting more careful examinations of the relationships between metabolic networks and host-pathogen interactions.

MOTIVATION: Drug effects are mainly caused by the interactions between drug molecules and their target proteins including primary targets and off-targets. Identification of the molecular mechanisms behind overall drug-target interactions is crucial in the drug design process. RESULTS: We develop a classifier-based approach to identify chemogenomic features (the underlying associations between drug chemical substructures and protein domains) that are involved in drug-target interaction networks. We propose a novel algorithm for extracting informative chemogenomic features by using L(1) regularized classifiers over the tensor product space of possible drug-target pairs. It is shown that the proposed method can extract a very limited number of chemogenomic features without loosing the performance of predicting drug-target interactions and the extracted features are biologically meaningful. The extracted substructure-domain association network enables us to suggest ligand chemical fragments specific for each protein domain and ligand core substructures important for a wide range of protein families. AVAILABILITY: Softwares are available at the supplemental website. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Datasets and all results are available at http://cbio.ensmp.fr/~yyamanishi/l1binary/ .

Robustness is one of the important features of living organisms. For example, many organisms have strong adaptability to environmental changes and many organisms can live even if some of their genes are mutated. Besides, it is considered that cancer cells are very robust and thus cancers are difficult to treat. Therefore, it is important to identify origins of robustness in various kinds of organisms.Though several methods have been proposed for measuring robustness in metabolic networks or signal transduction networks, most methods require large computation time or are not guaranteed to output optimal solutions.In this paper, we formalized the problem as an integer program, where an objective function is to minimize the number of reactions to be inactivated so that at least one of the target compounds cannot be synthesized. In order to cope with cycles and reversible reactions, we developed a novel integer programming formalization method using a feedback vertex set (FVS). When applied to an E. coli metabolic network consisting of Glycolysis/Glyconeogenesis, Citrate cycle and Pentose phosphate pathway obtained from KEGG database, we could find an optimal set of enzymes to be inactivated several times faster than a naive method.

MOTIVATION: Half-sphere exposure (HSE) is a newly developed two-dimensional solvent exposure measure. By conceptually separating an amino acid's sphere in a protein structure into two half spheres which represent its distinct spatial neighborhoods in the upward and downward directions, the HSE-up and HSE-down measures show superior performance compared with other measures such as accessible surface area, residue depth and contact number. However, currently there is no existing method for the prediction of HSE measures from sequence data. RESULTS: In this article, we propose a novel approach to predict the HSE measures and infer residue contact numbers using the predicted HSE values, based on a well-prepared non-homologous protein structure dataset. In particular, we employ support vector regression (SVR) to quantify the relationship between HSE measures and protein sequences and evaluate its prediction performance. We extensively explore five sequence-encoding schemes to examine their effects on the prediction performance. Our method could achieve the correlation coefficients of 0.72 and 0.68 between the predicted and observed HSE-up and HSE-down measures, respectively. Moreover, contact number can be accurately predicted by the summation of the predicted HSE-up and HSE-down values, which has further enlarged the application of this method. The successful application of SVR approach in this study suggests that it should be more useful in quantifying the protein sequence-structure relationship and predicting the structural property profiles from protein sequences. AVAILABILITY: The prediction webserver and supplementary materials are accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/hse/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: In recent years, an extensive characterization of network structures has been made in an effort to elucidate design principles of metabolic networks, providing valuable insights into the functional organization and the evolutionary history of organisms. However, previous analyses have not discussed the effects of environmental factors (i.e., exogenous forces) in shaping network structures. In this work, we investigate the effect of temperature, which is one of the environmental factors that may have contributed to shaping structures of metabolic networks. RESULTS: For this, we investigate the correlations between several structural properties characterized by graph metrics like the edge density, the degree exponent, the clustering coefficient, and the subgraph concentration in the metabolic networks of 113 prokaryotes and optimal growth temperature. As a result, we find that these structural properties are correlated with the optimal growth temperature. With increasing temperature, the edge density, the clustering coefficient and the subgraph concentration decrease and the degree exponent becomes large. CONCLUSION: This result implies that the metabolic networks transit with temperature as follows. The density of chemical reactions becomes low, the connectivity of the networks becomes homogeneous such as random networks and both the network modularity, based on the graph-theoretic clustering coefficient, and the frequency of recurring subgraphs decay. In short, metabolic networks undergo a change from heterogeneous and high-modular structures to homogeneous and low-modular structures, such as random networks, with temperature. This finding may suggest that the temperature plays an important role in the design principles of metabolic networks.