University of Idaho

Evolutionary therapies regulate heterogeneous populations by altering selective pressures through treatment sequences in cancer and infections. This letter develops an invariant-set framework for treatment-induced containment based on positive triangular invariant sets. For periodically switched systems, sufficient conditions are derived for the existence of such invariant regions. Robustness with respect to mutation is established by showing that the invariant simplex persists under small perturbations of the subsystem matrices. In the two-phenotype case, the analysis yields an explicit mutation threshold that separates regimes in which therapy cycling maintains containment from regimes in which mutation can enable evolutionary escape. Simulations illustrate the geometry of the invariant sets and the role of mutation and dwell time in containment robustness.

<ns7:p>The well-being of farmed and companion animals is increasingly recognised as integral to sustainable agroecosystems, companionship, and the One Health approach, which emphasises the interconnected health of people, animals, and the environment. The ELIXIR Domestic Animals Genome and Phenome (DAGP) Community supports genome-to-phenome analyses for farmed and companion animal species. Its aim is to coordinate, discuss, and explore the potential of data technology solutions to address key issues in animal welfare, behaviour, health, infectious diseases, metabolism, nutritional efficiency, and the preservation of genetic diversity and the environment. Through consolidating efforts to develop data standards, coordination, workflows, and visualisation, it will enhance the science underpinning rapidly growing fields in domestic animal genomics, including genome-enabled breeding, population genomics, pangenome analysis, functional genomics, genome editing, paleogenomics, phenotyping, and bio-banking. These standards will adhere to the FAIR data principles and leverage established ontologies to promote best practices in data coordination and archiving. This white paper, prepared by the ELIXIR DAGP Focus Group, summarises the current data infrastructure, resources, and tools available for domestic animal genomics and phenomics, and presents community-led plans and priorities to be implemented to meet the requirements of ELIXIR services and the animal science community. We describe how ELIXIR services can be applied in the domestic animal genomics and phenomics fields, and how we can connect projects and infrastructures that are active in the animal sciences domain. We also discuss three key priority areas: i) expanding the FAANG Data Portal for phenotype data with ELIXIR Data Platforms; ii) supporting submissions of new data types across ELIXIR Core Data Resources , including proprietary data from industry partners; and iii) strengthening connections to existing ELIXIR Communities and international consortia. This article provides a set of priorities for a Domestic Animals Genome and Phenome Community in ELIXIR and outlines the next steps to engage across stakeholders and to consolidate data for domestic animal science in Europe.</ns7:p>

Effective evaluation and assessment of research-focused programs, including centers, core facilities, and institutes, is crucial but inherently challenging. Faculty members are often departmentalized, yet their research necessitates campus-wide access to infrastructure, specialized equipment, students, and interdisciplinary collaborators, thus creating assessment and reporting challenges. Additionally, it can be challenging to demonstrate return on investment for new and emerging programs as well as those at institutions without an extensive history of research. Social network analysis (SNA) tools may be used to address these challenges. SNA provides a data-driven understanding of collaboration dynamics in programs designed to build research community and provides a method to quantify program impact and demonstrate the effectiveness of capacity-building investments in fostering collaborative research and scientific output. Additionally, SNA is useful in pinpointing influential researchers who act as central nodes or bridges, and may guide future efforts in mentorship, grant development, and recruitment, by highlighting areas of productive collaboration, or alternatively, potential fragmentation in the network. The results may be used to guide future resource allocation, inform new initiatives within the program, or alternatively, target interventions if needed to strengthen a specific research area. Initial analysis can help a program assess collaborative health of a research community by providing a baseline for tracking changes in network structure and collaboration patterns over time, setting the stage for longitudinal evaluation. This article outlines the steps required to carry out SNA of bibliometric data from a biomedical research community. The article describes the acquisition of data through PubMed, application of VOSviewer and Gephi, data visualization, statistical analysis, and generation of figures suitable for publication, including examples of results designed for specific audiences and stakeholders. The article also covers creating an author thesaurus for disambiguation of authors who publish under multiple names. © 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Obtaining data Basic Protocol 2: Initial bibliometric data processing and co-authorship network construction using VOSviewer Basic Protocol 3: Interfacing VOSviewer data with Gephi Basic Protocol 4: Advanced network visualization and statistical analysis in Gephi Basic Protocol 5: Creating impactful figures Basic Protocol 6: Identifying key individuals or groups of individuals within the network Support Protocol 1: VOSviewer author thesaurus file generation Support Protocol 2: Quick-start protocol: SNA for program evaluation.

Abstract Rapids are common in steep rivers, often forming where flow transitions from supercritical (Froude number, Fr &gt; 1) to subcritical ( Fr &lt; 1) through a hydraulic jump. When upstream Fr is supercritical but close to 1, this transition may occur as an undular hydraulic jump, exhibiting a train of stationary waves downstream of the jump toe. Previous studies proposed a method to estimate discharge using only UHJ wave spacing and channel width combined with a wave dispersion equation for large water depths relative to the UHJ wavelength. This method is based on the hypotheses that, by their presence, UHJs indicate near‐critical flow conditions ( Fr 1) and that wave celerity c is equal to and opposite the cross‐sectionally averaged flow velocity U . However, these hypotheses have not been thoroughly tested. We used data from published UHJ flume experiments to test the hypotheses that Fr 1 and c = U , compare the deep‐water and general wave dispersion equations, and evaluate the accuracy of discharge estimates. In these experiments, the stationary waves exhibited shallow depths relative to wavelength and flow was subcritical ( Fr &lt; 1) when averaged across multiple wavelengths. Additionally, wave celerity more closely approximated the surface flow velocity than U . By using a Fr representative of actual conditions and applying a coefficient to correct for , the accuracy of the discharge estimates improved. This finding suggests that the critical flow‐based method is robust and can produce reliable streamflow estimates if the remotely observed wave trains are correctly interpreted as UHJs, without requiring in situ measurements.