First-stage merging of Cartesian product clusters based on bipartite clustering.

(a) Bipartite clustering yields super-clusters, each containing multiple clusters in every view. A super-cluster is marked by a given color, and the same super color is shown by different shapes in the two views. Any super-cluster of interaction effects will be treated as a merged product cluster in later analysis. (b) The off-diagonal white blocks correspond to unmatched product (UP) super-clusters. The diagonal colored blocks correspond to matched product (MP) super-clusters. (c) A simple case that the true clusters are the product clusters from two views. The information from the two views is fully complementary.</p

UMAP visualization for HBMC data and the clustering results.

(a) True clusters on RNA. (b) Single-view clustering result on RNA. (c) CPS-merge analysis result on RNA. (d) True clusters on Protein (ADT). (e) Single-view clustering result on Protein (ADT). (f) CPS-merge analysis result on Protein (ADT).</p

UMAP visualization for PBMC1 data and the clustering results.

(a) Truth clusters on RNA. (b) Single-view clustering result on RNA. (c) CPS-merge analysis result on RNA. (d) Truth clusters on ATAC. (e) Single-view clustering result on ATAC. (f) CPS-merge analysis result on ATAC.</p

UMAP visualization for PBMC2 data and the clustering results.

(a) True clusters on RNA. (b) Single-view clustering result on RNA. (c) CPS-merge analysis result on RNA. (d) True clusters on Protein (ADT). (e) Single-view clustering result on Protein (ADT). (f) CPS-merge analysis result on Protein (ADT).</p

Summary of the three multi-view datasets after pre-processing.

Summary of the three multi-view datasets after pre-processing.</p

The pipeline of CPS-merge analysis.

When there are more than two views, users can either directly treat the Cartesian product clusters with higher orders or conduct step-wise merging such that two views are treated at each step. Current mutlimodal single-cell datasets only contain two views.</p

Clustering results on three muti-view datasets (HBMC, PBMC1 and PBMC2) obtained by 8 methods (first 8 columns).

Performance is measured by ARI, NMI and F-measure. Columns View 1 and View 2 are single-view clustering results on each dataset, where View 1 refers to RNA data in datasets and View 2 refers to ADT (protein) data in HBMC and PBMC2, and ATAC data in PBMC1. The highest ARI, NMI and F-measure achieved for each dataset are in bold.</p

This file contains the description of the simulation study and sensitivity analysis.

This file contains the description of the simulation study and sensitivity analysis.</p

The beneficial effects of dietary restriction on learning are distinct from its effects on longevity and mediated by depletion of a neuroinhibitory metabolite

<div><p>In species ranging from humans to <i>Caenorhabditis elegans</i>, dietary restriction (DR) grants numerous benefits, including enhanced learning. The precise mechanisms by which DR engenders benefits on processes related to learning remain poorly understood. As a result, it is unclear whether the learning benefits of DR are due to myriad improvements in mechanisms that collectively confer improved cellular health and extension of organismal lifespan or due to specific neural mechanisms. Using an associative learning paradigm in <i>C</i>. <i>elegans</i>, we investigated the effects of DR as well as manipulations of insulin, mechanistic target of rapamycin (mTOR), AMP-activated protein kinase (AMPK), and autophagy pathways—processes implicated in longevity—on learning. Despite their effects on a vast number of molecular effectors, we found that the beneficial effects on learning elicited by each of these manipulations are fully dependent on depletion of kynurenic acid (KYNA), a neuroinhibitory metabolite. KYNA depletion then leads, in an N-methyl D-aspartate receptor (NMDAR)-dependent manner, to activation of a specific pair of interneurons with a critical role in learning. Thus, fluctuations in KYNA levels emerge as a previously unidentified molecular mechanism linking longevity and metabolic pathways to neural mechanisms of learning. Importantly, KYNA levels did not alter lifespan in any of the conditions tested. As such, the beneficial effects of DR on learning can be attributed to changes in a nutritionally sensitive metabolite with neuromodulatory activity rather than indirect or secondary consequences of improved health and extended longevity.</p></div

Synthesis of Diarylmethanes via Metal-Free Reductive Cross-Coupling of Diarylborinic Acids with Tosyl Hydrazones

This paper describes a practical and efficient procedure that takes advantage of diarylborinic acids as a cost-effective alternative to arylboronic acids for synthesis of diarylmethanes through metal-free reductive cross-coupling with <i>N</i>-tosylhydrazones of aromatic aldehydes and ketones. The procedure tolerates hydroxyl, halide, amine, and allyl functionality, complementary to the transition-metal catalyzed cross-coupling techniques