Search for: All records

Abstract Motivation:Clustering patients into subgroups based on their microbial compositions can greatly enhance our understanding of the role of microbes in human health and disease etiology. Distance-based clustering methods, such as partitioning around medoids (PAM), are popular due to their computational efficiency and absence of distributional assumptions. However, the performance of these methods can be suboptimal when true cluster memberships are driven by differences in the abundance of only a few microbes, a situation known as the sparse signal scenario. Results:We demonstrate that classical multidimensional scaling (MDS), a widely used dimensionality reduction technique, effectively denoises microbiome data and enhances the clustering performance of distance-based methods. We propose a two-step procedure that first applies MDS to project high-dimensional microbiome data into a low-dimensional space, followed by distance-based clustering using the low-dimensional data. Our extensive simulations demonstrate that our procedure offers superior performance compared to directly conducting distance-based clustering under the sparse signal scenario. The advantage of our procedure is further showcased in several real data applications. Availability and implementation:The R package MDSMClust is available at https://github.com/wxy929/MDS-project. 

Thehydrogenoxidationreaction(HOR)inalkalineelectrolytesexhibitsmarkedlyslowerkineticsthanthatinacidic electrolytes.Thisposesacriticalchallengeforalkalineexchangemembranefuelcells(AEMFCs).Theslowerkineticsinalkaline electrolytesisoftenattributedtothemoresluggishVolmerstep(hydrogendesorption).IthasbeenshownthatthealkalineHOR activityonthePtsurfacecanbeconsiderablyenhancedbythepresenceofoxophilictransitionmetals(TMs)andsurface-adsorbed hydroxylgroupsonTMs(TM−OHad),althoughtheexactroleofTM−OHadremainsatopicofactivedebates.Herein,usingsingle- atomRh-tailoredPtnanowiresasamodelsystem,wedemonstratethathydroxylgroupsadsorbedontheRhsites(Rh−OHad)can profoundly reorganize the Pt surface water structure to deliver a record-setting alkaline HOR performance. In situ surface characterizations,togetherwiththeoreticalstudies,revealthatsurfaceRh−OHadcouldpromotetheoxygen-downwater(H2O↓)that favorsmorehydrogenbondwithPtsurfaceadsorbedhydrogen(H2O↓···Had-Pt)thanthehydrogen-downwater(OH2↓).TheH2O↓ furtherservesasthebridgetofacilitatetheformationofanenergeticallyfavorablesix-membered-ringtransitionstructurewith neighboringPt−Had andRh−OHad,thusreducingtheVolmerstepactivationenergyandboostingHORkinetics. 

Abstract The U.S. coastlines have experienced rapid increases in occurrences of High Tide Flooding (HTF) during recent decades. While it is generally accepted that relative mean sea level (RMSL) rise is the dominant cause for this, an attribution to individual components is still lacking. Here, we use local sea-level budgets to attribute past changes in HTF days to RMSL and its individual contributions. We find that while RMSL rise generally explains > 84% of long-term increases in HTF days locally, spatial patterns in HTF changes also depend on differences in flooding thresholds and water level characteristics. Vertical land motion dominates long-term increases in HTF, particularly in the northeast, while sterodynamic sea level (SDSL) is most important elsewhere and on shorter temporal scales. We also show that the recent SDSL acceleration in the Gulf of Mexico has led to an increase of 220% in the frequency of HTF events over the last decade. 

Abstract While there is evidence for an acceleration in global mean sea level (MSL) since the 1960s, its detection at local levels has been hampered by the considerable influence of natural variability on the rate of MSL change. Here we report a MSL acceleration in tide gauge records along the U.S. Southeast and Gulf coasts that has led to rates (>10 mm yr −1 since 2010) that are unprecedented in at least 120 years. We show that this acceleration is primarily induced by an ocean dynamic signal exceeding the externally forced response from historical climate model simulations. However, when the simulated forced response is removed from observations, the residuals are neither historically unprecedented nor inconsistent with internal variability in simulations. A large fraction of the residuals is consistent with wind driven Rossby waves in the tropical North Atlantic. This indicates that this ongoing acceleration represents the compounding effects of external forcing and internal climate variability. 

Abstract Classical multidimensional scaling is a widely used dimension reduction technique. Yet few theoretical results characterizing its statistical performance exist. This paper provides a theoretical framework for analyzing the quality of embedded samples produced by classical multidimensional scaling. This lays a foundation for various downstream statistical analyses, and we focus on clustering noisy data. Our results provide scaling conditions on the signal-to-noise ratio under which classical multidimensional scaling followed by a distance-based clustering algorithm can recover the cluster labels of all samples. Simulation studies confirm these scaling conditions are sharp. Applications to the cancer gene-expression data, the single-cell RNA sequencing data and the natural language data lend strong support to the methodology and theory.