S Organ

Treatment Pluralism in Full-Scale UV LED Wastewater Disinfection Showcasing Flexible Flow and Energy Control with Predictable Performance

Bailey Reid, Sean A. MacIsaac, S Organ, C. Carolina Ontiveros et autres

Wastewater treatment facilities (WWTFs) are currently facing multitudes of challenges including incline in population, climate change driven challenges and stringent regulation limits. Ultraviolet light emitting diodes (UV LEDs) are becoming an attractive disinfection system that has demonstrated powerful inactivation capabilities with modular …

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Microbial predictors of sustained clinical remission following the Crohn’s disease exclusion diet in adults and microbiome shifts toward healthy pediatric controls

Rotem Sigall-Boneh, S Organ, Mohammed Ghiboub, Henit Yanai et autres

Abstract Background The Crohn’s disease (CD) exclusion diet (CDED) is an emerging dietary therapy for inducing remission in CD. However, data on its effects on gut microbiome in adults remain limited. This study investigated microbial responses to CDED in adults with mild-to-moderate …

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HVS: Hypergraph Variable Selection

S Organ

Performs hypergraph-based setwise variable selection with false discovery rate control (Organ, Kenney & Gu, 2026, <doi:10.48550/arXiv.2606.20514>). The idea is, in addition to selecting individual predictors when there is sufficient evidence, to also test all pairs of predictors, and when there is insufficient …

Hypergraph Variable Selection with False Discovery Rate Control

S Organ, Toby Kenney, Hong Gu

Variable selection methods that control the false discovery rate often lose power when predictors exhibit complex dependence structures. We previously showed that selecting hierarchically clustered groups of predictors can mitigate this issue while maintaining false discovery rate control. When correlations are less …

Hypergraph Variable Selection with False Discovery Rate Control

S Organ, Toby Kenney, Hong Gu

Variable selection methods that control the false discovery rate often lose power when predictors exhibit complex dependence structures. We previously showed that selecting hierarchically clustered groups of predictors can mitigate this issue while maintaining false discovery rate control. When correlations are less …

ca (code pays fourni par la source)

Data-driven decision support identifies treatment optimization pathways for municipal wastewater

S Organ, Jane McDonald, Het Mehta, María Ibáñez et autres

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SHRED: Setwise Hierarchical Rate of Erroneous Discovery

S Organ, Hong Gu

Setwise Hierarchical Rate of Erroneous Discovery (SHRED) methods for setwise variable selection with false discovery rate (FDR) control. Setwise variable selection means that sets of variables may be selected when the true variable cannot be identified. This allows us to maintain FDR …

Setwise Hierarchical Variable Selection and the Generalized Linear Step-Up Procedure for False Discovery Rate Control

S Organ, Toby Kenney, Hong Gu

Controlling the false discovery rate (FDR) in variable selection becomes challenging when predictors are correlated, as existing methods often exclude all members of correlated groups and consequently perform poorly for prediction. We introduce a new setwise variable-selection framework that identifies clusters of …

Setwise Hierarchical Variable Selection and the Generalized Linear Step-Up Procedure for False Discovery Rate Control

S Organ, Toby Kenney, Hong Gu

Controlling the false discovery rate (FDR) in variable selection becomes challenging when predictors are correlated, as existing methods often exclude all members of correlated groups and consequently perform poorly for prediction. We introduce a new setwise variable-selection framework that identifies clusters of …

ca (code pays fourni par la source)

Data-Driven Decision Support Identifies Treatment Optimization Pathways for Municipal Wastewater

S Organ, Jane McDonald, Het Mehta, Maria Ibanez et autres

Bench-scale experimental data and scripts for running the DSS application in R studio.

Data-Driven Decision Support Identifies Treatment Optimization Pathways for Municipal Wastewater

S Organ, Jane McDonald, Het Mehta, Maria Ibanez et autres

Bench-scale experimental data and scripts for running the DSS application in R studio.

Generalizing the Linear Step-up Procedure for False Discovery Rate Control with Applications to Setwise and High Dimensional Variable Selection

S Organ

This thesis presents a unified framework for false discovery rate (FDR) controlled variable selection that addresses three major challenges: (1) the inability of traditional FDR procedures to accommodate structured, non-independent hypothesis testing, (2) the failure of standard FDR control procedures for variable …

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