Humanigen (United States)

Abstract Cell-free RNA (cfRNA) in human plasma provides a minimally invasive readout of tissue physiology, yet its extreme sparsity, heavy-tailed abundance distributions, and weak but structured correlation patterns create major challenges for machine learning. Conventional tabular foundation models are typically trained on synthetic datasets that assume generic statistical properties, and as a result, they fail to capture the distinctive characteristics of cfRNA. These limitations become even more pronounced in settings where labeled data are scarce. We introduce cfRNA-ICL , a cfRNA-specific in-context learning model trained entirely on tasks generated from a biologically grounded structural causal model (SCM) . The SCM produces realistic cfRNA-like scenarios by incorporating empirical measurements of gene-level dropout, overdispersion, tissue-mixture–driven latent factors, compositional variability, and sequencing noise. This synthetic task universe enables cfRNA-ICL to acquire inductive biases that closely reflect the geometry of real cfRNA data. Across multiple cancer classification benchmarks, cfRNA-ICL demonstrates consistently higher performance than tabular ICL models trained on generic synthetic data. The gains are most sub-stantial in few-shot settings, where the model benefits from its exposure to cfRNA-specific statistical regimes during meta-training. Representation-level analyses further show that cfRNA-ICL organizes samples into biologically coherent manifolds, preserving cancer-type identity without the use of supervised constraints. This finding indicates strong alignment between the synthetic prior and real cfRNA structure. Taken together, these results show that domain-aware generative priors can meaningfully enhance in-context learning for biological tabular data. cfRNA-ICL provides a generalizable framework for cfRNA modeling and establishes a practical path toward foundation-scale models that are intrinsically adapted to the statistical landscape of plasma cfRNA.

ICLAS Laboratory Animal Quality Network (LAQN) programs currently consist of the Performance Evaluation Program (PEP), which focuses on microbial monitoring by and for laboratory animal diagnostic laboratories, and the Genetic Reference Monitoring Program (GENRef), which provides assay-ready reference DNA for genetic testing of mouse strains. Since 2008, PEP has grown to become a truly international program with participating laboratories in 5 continents. Launched in 2016, GENRef currently distributes DNA from 12 common inbred mouse strains for use in genetic monitoring of locally inbred colonies as well as for genetic testing of stocks, particularly genetically engineered stocks, of uncertain origins. GENRef has the capacity to include additional strains as well as additional species. PEP and GENRef provide the reagents at cost, as a resource to the international scientific community, in the interest of improving research quality in an environment of growing concern for research quality, rigor, and reproducibility.

Abstract Biochemical Differences in Maize as Related to Mineral Deficiencies. I. Changes in the Activity of some Enzymes. — The enzymatic systems in the different organs of corn seedlings are variously affected in the first growing stages by deficiency in nitrogen, phosphorus or potassium, according to the specific physiological functions of the organs. Deficiency in nitrogen causes an increase in peroxidase and catalase activity and inhibition of pyruvic and glutamic decarboxylase. Deficiency in phosphorus causes an activation of peroxidase and catalase in leaves and an inhibition of acid phosphatasc, invertase, pyruvic and glutatnic decarboxylase in roots. Deficiency in potassium, as compared to deficiency in nitrogen and phosphorus, leads to the severest disturbances of the different enzymatic systems. This deficiency causes a strong increase in leaf invertase, catalase and peroxidase activity, a stimulus in respiration intensity in the roots, and an inhibition in pyruvic and glutaniic decarboxylase in leaves and roots. The activation and inhibition changes in the enzymatic processes, appearing as a consequence of nitrogen, phosphorus and potassium deficiency have different significance and intensity.