Learning High-Dimensional Nonparametric Differential Equations via Multivariate Occupation Kernel Functions

Learning a nonparametric system of ordinary differential equations (ODEs) from $n$ trajectory snapshots in a $d$-dimensional state space requires learning $d$ functions of $d$ variables. Explicit formulations scale quadratically in $d$ unless additional knowledge about system properties, such as sparsity and symmetries, is available. In this work, we propose a linear approach to learning using the implicit formulation provided by vector-valued Reproducing Kernel Hilbert Spaces. By rewriting the ODEs in a weaker integral form, which we subsequently minimize, we derive our learning algorithm. The minimization problem's solution for the vector field relies on multivariate occupation kernel functions associated with the solution trajectories. We validate our approach through experiments on highly nonlinear simulated and real data, where $d$ may exceed 100. We further demonstrate the versatility of the proposed method by learning a nonparametric first order quasilinear partial differential equation.Comment: 22 pages, 3 figures, submitted to Neurips 202

Learning nonparametric ordinary differential equations from noisy data

Learning nonparametric systems of Ordinary Differential Equations (ODEs) dot x = f(t,x) from noisy data is an emerging machine learning topic. We use the well-developed theory of Reproducing Kernel Hilbert Spaces (RKHS) to define candidates for f for which the solution of the ODE exists and is unique. Learning f consists of solving a constrained optimization problem in an RKHS. We propose a penalty method that iteratively uses the Representer theorem and Euler approximations to provide a numerical solution. We prove a generalization bound for the L2 distance between x and its estimator and provide experimental comparisons with the state-of-the-art.Comment: 25 pages, 6 figure

Clinical Consensus Conference: Survey on Gram-Positive Bloodstream Infections with a Focus on Staphylococcus aureus

The increased incidence over the past decade of bloodstream infections (BSIs) caused by gram-positive bacteria, particularly methicillin-resistant Staphylococcus aureus , highlights the critical need for a consistent approach to therapy. However, there is currently no international consensus on the diagnosis and management of gram-positive BSIs. The Clinical Consensus Conference on Gram-Positive Bloodstream Infections was convened as a session at the 9th International Symposium on Modern Concepts in Endocarditis and Cardiovascular Infections held in 2007. Participants discussed various aspects of the practical treatment of patients who present with gram-positive BSI, including therapeutic options for patients with BSIs of undefined origin, the selection of appropriate empirical therapy, and treatment of complicated and uncomplicated BSIs. The opinions of participants about these key issues are reflected in this articl

Learning Nonparametric Ordinary Differential Equations: Application to Sparse and Noisy Data

Learning nonparametric systems of Ordinary Differential Equations (ODEs) x˙=f(t,x) from noisy and sparse data is an emerging machine learning topic. We use the well-developed theory of Reproducing Kernel Hilbert Spaces (RKHS) to define candidates for f for which the solution of the ODE exists and is unique. Learning f consists of solving a constrained optimization problem in an RKHS. We propose a penalty method that iteratively uses the Representer theorem and Euler approximations to provide a numerical solution. We prove a generalization bound for the L2 distance between x and its estimator. Experiments are provided for the FitzHugh Nagumo oscillator and for the prediction of the Amyloid level in the cortex of aging subjects. In both cases, we show competitive results when compared with the state of the art

Identifying the Potential for Cross-Fishery Spillovers: A Network Analysis of Alaskan Permitting Patterns, Working Paper, Resources for the Future

Many fishermen own a portfolio of permits across multiple fisheries, creating an opportunity for fishing effort to adjust across fisheries and enabling impacts from a policy change in one fishery to spill over into other fisheries. In regions with a large and diverse number of permits and fisheries, joint-permitting can result in a complex system, making it difficult to understand the potential for cross-fishery substitution. In this study, we construct a network representation of permit ownership to characterize interconnectedness between Alaska commercial fisheries due to cross-fishery permitting. The Alaska fisheries network is highly connected, suggesting that most fisheries are vulnerable to cross-fishery spillovers from network shocks, such as changes to policies or fish stocks. We find that fisheries with similar geographic proximity are more likely to be a part of a highly connected cluster of susceptible fisheries. We use a case study to show that preexisting network statistics can be useful for identifying the potential scope of policy-induced spillovers. Our results demonstrate that network analysis can improve our understanding of the potential for policy-induced cross-fishery spillovers.Introduction / Material and Methods / Results and Discussion / Conclusion / References / Tables and Figure

Identifying the Potential for Cross-Fishery Spillovers: A Network Analysis of Alaskan Permitting Patterns

Many fishermen own a portfolio of permits across multiple fisheries, creating an opportunity for fishing effort to adjust across fisheries and enabling impacts from a policy change in one fishery to spillover into other fisheries. In regions with a large and diverse number of permits and fisheries, joint-permitting can result in a complex system, making it difficult to understand the potential for cross-fishery substitution. In this study, we construct a network representation of permit ownership to characterize interconnectedness between Alaska commercial fisheries due to cross-fishery permitting. The Alaska fisheries network is highly connected, suggesting that most fisheries are vulnerable to cross-fishery spillovers from network shocks, such as changes to policies or fish stocks. We find that fisheries with similar geographic proximity are more likely to be a part of a highly connected cluster of susceptible fisheries. We use a case study to show that preexisting network statistics can be useful for identifying the potential scope of policy-induced spillovers. Our results demonstrate that network analysis can improve our understanding of the potential for policy-induced cross-fishery spillovers.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Previous infection with seasonal coronaviruses does not protect male Syrian hamsters from challenge with SARS-CoV-2

Abstract SARS-CoV-2 variants and seasonal coronaviruses continue to cause disease and coronaviruses in the animal reservoir pose a constant spillover threat. Importantly, understanding of how previous infection may influence future exposures, especially in the context of seasonal coronaviruses and SARS-CoV-2 variants, is still limited. Here we adopted a step-wise experimental approach to examine the primary immune response and subsequent immune recall toward antigenically distinct coronaviruses using male Syrian hamsters. Hamsters were initially inoculated with seasonal coronaviruses (HCoV-NL63, HCoV-229E, or HCoV-OC43), or SARS-CoV-2 pango B lineage virus, then challenged with SARS-CoV-2 pango B lineage virus, or SARS-CoV-2 variants Beta or Omicron. Although infection with seasonal coronaviruses offered little protection against SARS-CoV-2 challenge, HCoV-NL63-infected animals had an increase of the previously elicited HCoV-NL63-specific neutralizing antibodies during challenge with SARS-CoV-2. On the other hand, primary infection with HCoV-OC43 induced distinct T cell gene signatures. Gene expression profiling indicated interferon responses and germinal center reactions to be induced during more similar primary infection-challenge combinations while signatures of increased inflammation as well as suppression of the antiviral response were observed following antigenically distant viral challenges. This work characterizes and analyzes seasonal coronaviruses effect on SARS-CoV-2 secondary infection and the findings are important for pan-coronavirus vaccine design

Azithromycin promotes relapse by disrupting immune and metabolic networks after allogeneic stem cell transplantation

International audienceAdministration of azithromycin after allogeneic hematopoietic stem cell transplantation for hematological malignancies has been associated with relapse in a randomized phase 3 controlled clinical trial. Studying 240 samples from patients randomized in this trial is a unique opportunity to better understand the mechanisms underlying relapse, the first cause of mortality after transplantation. We used multi-omics on patients' samples to decipher immune alterations associated with azithromycin intake and post-transplant relapsed malignancies. Azithromycin was associated with a network of altered energy metabolism pathways and immune subsets, including T cells biased toward immunomodulatory and exhausted profiles. In vitro, azithromycin exposure inhibited T cells cytotoxicity against tumor cells and impaired T cells metabolism through glycolysis inhibition, mitochondrial genes downregulation, and immunomodulatory genes upregulation, notably SOCS1. These results highlight that azithromycin directly affects immune cells that favor relapse, which raises caution about long-term use of azithromycin treatment in patients at high risk of malignancies

Clinical Consensus Conference: Survey on Gram-Positive Bloodstream Infections with a Focus on Staphylococcus aureus

The increased incidence over the past decade of bloodstream infections (BSIs) caused by gram-positive bacteria, particularly methicillin-resistant Staphylococcus aureus, highlights the critical need for a consistent approach to therapy. However, there is currently no international consensus on the diagnosis and management of gram-positive BSIs. The Clinical Consensus Conference on Gram-Positive Bloodstream Infections was convened as a session at the 9th International Symposium on Modern Concepts in Endocarditis and Cardiovascular Infections held in 2007. Participants discussed various aspects of the practical treatment of patients who present with gram-positive BSI, including therapeutic options for patients with BSIs of undefined origin, the selection of appropriate empirical therapy, and treatment of complicated and uncomplicated BSIs. The opinions of participants about these key issues are reflected in this article