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Abstract How can the NGO–NGO interactions of Global South organizations be better understood and improved? Global South nongovernmental organizations (NGOs) have been frequently missing from the overall advocacy network. When they are included, global South organizations are less likely to take on brokerage roles that may help them build connections and resources across communities. Instead, if they are included, Global South NGOs often are relegated to less powerful, intra-community brokerage roles. Drawing on Deloffre and Quack’s framework (Chapter 1, this volume), Chapter 6 examines the top Global South NGOs that have been able to overcome exclusionary structures and forge inter-community brokerage connections to other NGOs. A deeper look at these organizations and the structures where they are embedded can help to gain insights into the transformative nature of NGO–NGO interactions. The chapter finds that certain country, community, and organizational factors help some Global South NGOs develop connections outside of their immediate community. A focus on these factors may help innovation and protect against a civil society backlash. 

Abstract Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data. 

Abstract Optimal transport (OT) methods seek a transformation map (or plan) between two probability measures, such that the transformation has the minimum transportation cost. Such a minimum transport cost, with a certain power transform, is called the Wasserstein distance. Recently, OT methods have drawn great attention in statistics, machine learning, and computer science, especially in deep generative neural networks. Despite its broad applications, the estimation of high‐dimensional Wasserstein distances is a well‐known challenging problem owing to the curse‐of‐dimensionality. There are some cutting‐edge projection‐based techniques that tackle high‐dimensional OT problems. Three major approaches of such techniques are introduced, respectively, the slicing approach, the iterative projection approach, and the projection robust OT approach. Open challenges are discussed at the end of the review. This article is categorized under:Statistical and Graphical Methods of Data Analysis > Dimension ReductionStatistical Learning and Exploratory Methods of the Data Sciences > Manifold Learning 

A SERS instrument transformation framework based on the penalized functional regression model (SpectraFRM) is proposed for cross-instrument mapping with subsequent machine learning classification to compare transformed spectra with standard spectra.