Stochastic climate theory and modeling

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large-scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modeling. In this review, we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspective. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models

Next-Generation Sequencing in Veterinary Medicine: How Can the Massive Amount of Information Arising from High-Throughput Technologies Improve Diagnosis, Control, and Management of Infectious Diseases?

The development of high-throughput molecular technologies and associated bioinformatics has dramatically changed the capacities of scientists to produce, handle, and analyze large amounts of genomic, transcriptomic, and proteomic data. A clear example of this step-change is represented by the amount of DNA sequence data that can be now produced using next-generation sequencing (NGS) platforms. Similarly, recent improvements in protein and peptide separation efficiencies and highly accurate mass spectrometry have promoted the identification and quantification of proteins in a given sample. These advancements in biotechnology have increasingly been applied to the study of animal infectious diseases and are beginning to revolutionize the way that biological and evolutionary processes can be studied at the molecular level. Studies have demonstrated the value of NGS technologies for molecular characterization, ranging from metagenomic characterization of unknown pathogens or microbial communities to molecular epidemiology and evolution of viral quasispecies. Moreover, high-throughput technologies now allow detailed studies of host-pathogen interactions at the level of their genomes (genomics), transcriptomes (transcriptomics), or proteomes (proteomics). Ultimately, the interaction between pathogen and host biological networks can be questioned by analytically integrating these levels (integrative OMICS and systems biology). The application of high-throughput biotechnology platforms in these fields and their typical low-cost per information content has revolutionized the resolution with which these processes can now be studied