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PubMed2026

From complexity to clarity: Building dashboards for hit selection in high throughput screens.

High throughput screening produces large, complex datasets that are difficult to interrogate without programming expertise, making hit selection time-consuming and inflexible. While instrument software and commercial tools offer partial solutions, they often lack adaptability or require costly infrastructure. Interactive dashboards provide an effective alternative by enabling dynamic filtering and integrated visualization within a single interface. Here, we present simple R Markdown-based templates for creating customizable, modular dashboards for screen data analysis. Built using the flexdashboard and crosstalk R packages, and HTML widgets, these lightweight, easy-to-build HTML dashboards require no complex installation process or installation of licensed software. They support linked visualizations, threshold-based filtering (e.g., Z-score, p-value, fold change), and interactive data exploration and are shared as a standalone HTML file. This framework enables rapid, flexible hit selection across diverse high throughput screening applications and is designed for users with basic R experience.

High-Throughput Screening Assays↗
PubMed2026

Global inequities in hepatitis B and C genomic surveillance revealed through an interactive data integration dashboard.

OBJECTIVES: To assess global disparities in hepatitis B virus (HBV) and hepatitis C virus (HCV) genomic surveillance and to develop an integrated platform that links genomic data with epidemiological burden. STUDY DESIGN: Retrospective observational analysis. METHODS: We reviewed existing viral genomic repositories to identify structural and analytical limitations. Subsequently, we integrated 10 996 HBV and 3533 HCV whole-genome sequences (WGS) from public databases with Global Burden of Disease (GBD) estimates to quantify inequities in genomic surveillance across countries and genotypes. Using these data, we developed the open-access Hepatitis Dashboard, incorporating >14 000 sequences from 141 countries with GBD metrics to evaluate representativeness and sequencing coverage relative to disease burden. RESULTS: Marked inequities in hepatitis genomic surveillance were identified. Despite increasing HBV- and HCV-associated mortality, virus sequence availability remains geographically and genotypically skewed-dominated by China and the United States, with substantial underrepresentation of HBV genotype E and HCV genotypes 5 and 8. Many high-endemic countries in Africa and the Western Pacific remain severely undersampled. We detected circulating antiviral drug-resistance mutations and developed a burden-adjusted sequencing coverage metric, revealing that several high-burden countries, including China, Nigeria and India, are among the least represented in global genomic datasets. Projections to 2030 indicate that neither HBV nor HCV are currently on track to meet WHO elimination targets. CONCLUSIONS: The Hepatitis Dashboard provides an integrated, continuously updated resource that links genomic and epidemiological data to quantify and visualise global surveillance gaps. This analysis highlights a critical disconnect between sequencing efforts and public health needs, which may limit the effectiveness of surveillance-informed strategies to support progress toward WHO 2030 elimination goals. By enabling burden-adjusted prioritisation and longitudinal tracking of genomic coverage, the platform supports evidence-based sampling strategies, equitable resource allocation, and monitoring of global progress toward hepatitis elimination.

Humans↗
PubMed2026

National Antimicrobial Resistance Monitoring System: Three Decades of Advancing Public Health Through Integrated Surveillance of Antimicrobial Resistance.

Antimicrobial resistance (AMR) occurs when bacteria and other microorganisms adapt in ways that make medicines less effective, causing infections that are harder to treat and more likely to spread. According to the Centers for Disease Control and Prevention (CDC), AMR infections affect millions of Americans each year and contribute to thousands of deaths (CDC, 2019). After three decades of operation, the U.S. National Antimicrobial Resistance Monitoring System (NARMS) stands as a model of sustained, collaborative public health surveillance. What began in 1996 as an effort to track resistance in Salmonella and E. coli O157 has evolved into a One Health surveillance network monitoring AMR across the farm-to-fork continuum. Through a partnership among CDC, the Food and Drug Administration (FDA), the U.S. Department of Agriculture (USDA), state and local health departments, and universities, NARMS has become the backbone of foodborne AMR surveillance in the United States. The past decade has been particularly transformative. NARMS explored new sampling to include companion animals, minor livestock, aquaculture, surface water, and wildlife. Whole-genome sequencing (WGS) revolutionized the program's capabilities, enabling timely identification of emerging pathogens and revealing how resistance genes spread. Near real-time public dashboards make NARMS data accessible to researchers, clinicians, regulators, and policymakers. NARMS data shape decisions about new animal drug approvals, guide stewardship programs, and inform clinical treatment guidelines nationwide. As NARMS enters its fourth decade with a 2026-2030 strategic plan, the program will leverage artificial intelligence and metagenomics while expanding surveillance to fill remaining gaps ensuring this vital system continues to protect the food supply and both human and animal health from AMR.

Antimicrobial Resistance (AMR)↗
PubMed2026

Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation.

Bioacoustics is increasingly shifting from a mostly descriptive pursuit to one that can anticipate ecological change. Recent innovations-from autonomous recording units and edge-computing sensors to speech-inspired feature extraction and machine-learning techniques like transfer learning, unsupervised discovery, and explainable AI-are transforming the study of animal communication. These advances let us work at scales previously difficult to imagine. Automated species recognition, individual identification, and even tracking cultural evolution over decades are now within reach. Entire ecosystem soundscapes can be mapped with unprecedented resolution. Looking ahead, global listening networks, adaptive acoustic indices, and live biodiversity dashboards seem increasingly realistic. We may soon build digital models that simulate communication networks under future scenarios. Closer integration with genomics, physiology, and robotics could link vocal traits to their genetic, physiological, and ecological drivers. Challenges remain, including data governance, acoustic privacy, and equitable access to the planet's sonic heritage. Bioacoustics may be on the way to becoming a predictive, integrative science - one particularly well suited to monitoring, interpreting, and helping safeguard life's communication systems in a rapidly changing world.

Animals↗