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Oceanic eddies accompanied by a significant vertical velocity ( w ) are known to be of great importance for the vertical transport of various climatically, biologically or biogeochemically relevant properties. Using quasi-geostrophic w -thinking to extend the classic “ β -spiral” w -theory for gyre circulations to isolated and nearly symmetric oceanic mesoscale eddies, we propose that their w motion will be dominated by a strong east-west dipole pattern with deep ocean penetrations. Contrasting numerical simulations of idealized isolated eddies together with w -equation diagnostics confirm that the w -dipole is indeed dominated by the “eddy β -spiral” mechanism in the β -plane simulation, whereas this w -dipole expectedly disappears in the f -plane simulation. Analyses of relatively isolated warm and cold eddy examples show good agreement with the proposed mechanism. Our studies further clarify eddy vertical motions, have implications for ocean mixing and vertical transport, and inspire further studies. 

Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased episodic or chronic saltwater exposure, leading to shifts in biogeochemistry, plant- and microbial communities, as well as ecological services. Yet, it is still difficult to predict how soil microbial communities respond to the saltwater exposure because of poorly understood microbial sensitivity within complex wetland soil microbial communities, as well as the high spatial and temporal heterogeneity of wetland soils and saltwater exposure. To address this, we first conducted a two-year survey of microbial community structure and bottom water chemistry in submerged surface soils from 14 wetland sites across the Florida Everglades. We identified ecosystem-specific microbial biomarker taxa primarily associated with variation in salinity. Bacterial, archaeal and fungal community composition differed between freshwater, mangrove, and marine seagrass meadow sites, irrespective of soil type or season. Especially, methanogens, putative denitrifying methanotrophs and sulfate reducers shifted in relative abundance and/or composition between wetland types. Methanogens and putative denitrifying methanotrophs declined in relative abundance from freshwater to marine wetlands, whereas sulfate reducers showed the opposite trend. A four-year experimental simulation of saltwater intrusion in a pristine freshwater site and a previously saltwater-impacted site corroborated the highest sensitivity and relative increase of sulfate reducers, as well as taxon-specific sensitivity of methanogens, in response to continuously pulsing of saltwater treatment. Collectively, these results suggest that besides increased salinity, saltwater-mediated increased sulfate availability leads to displacement of methanogens by sulfate reducers even at low or temporal salt exposure. These changes of microbial composition could affect organic matter degradation pathways in coastal freshwater wetlands exposed to sea-level rise, with potential consequences, such as loss of stored soil organic carbon. 

This study offers the first investigation on the normative processes through which Chinese form impressions of others in social interaction. Using affect control theory and its archived sentiment data from China, I estimate the Chinese impression formation models with a new Bayesian method. I then compare the Chinese models to the impression formation dynamics in U.S. English. Results show cross-cultural commonality in the affective processing of cultural concepts, with determinants of impression formation processes being largely universal. Findings also reveal two cultural variations that align with patterns uncovered by comparative cross-cultural research: 1) the Chinese models show less rigidity in the definition of situation; and 2) across two cultural models, the balance term has opposite effects on actor and behavior evaluation. To explore the implications of the impression models, I present a series of simulations, illustrating the predictive power of affect control theory as well as the impact of different cultural rules on social interaction. 

We review Affect Control Theory (ACT), a mathematically formalized theory that integrates sociological insights about the symbolic construction of the social order with psychological knowledge about cognitive-affective mechanisms, as a basis for equipping computational agents in social simulations with a sense of sociality. After explaining theoretical foundations and describing previous applications of ACT at the dyadic and group level, we describe a case study from an ongoing research project aimed at understanding self-organized online collaboration in software development with ACT-based social simulations. 

Comprehensive and accurate comparisons of transcriptomic distributions of cells from samples taken from two different biological states, such as healthy versus diseased individuals, are an emerging challenge in single-cell RNA sequencing (scRNA-seq) analysis. Current methods for detecting differentially abundant (DA) subpopulations between samples rely heavily on initial clustering of all cells in both samples. Often, this clustering step is inadequate since the DA subpopulations may not align with a clear cluster structure, and important differences between the two biological states can be missed. Here, we introduce DA-seq, a targeted approach for identifying DA subpopulations not restricted to clusters. DA-seq is a multiscale method that quantifies a local DA measure for each cell, which is computed from its k nearest neighboring cells across a range of k values. Based on this measure, DA-seq delineates contiguous significant DA subpopulations in the transcriptomic space. We apply DA-seq to several scRNA-seq datasets and highlight its improved ability to detect differences between distinct phenotypes in severe versus mildly ill COVID-19 patients, melanomas subjected to immune checkpoint therapy comparing responders to nonresponders, embryonic development at two time points, and young versus aging brain tissue. DA-seq enabled us to detect differences between these phenotypes. Importantly, we find that DA-seq not only recovers the DA cell types as discovered in the original studies but also reveals additional DA subpopulations that were not described before. Analysis of these subpopulations yields biological insights that would otherwise be undetected using conventional computational approaches. 

This paper explores how individuals' privacy-related decision-making processes may be influenced by their pre-existing relationships to companies in a wider social and economic context. Through an online role-playing exercise, we explore attitudes to a range of services including home automation, Internet-of-Things and financial services. We find that individuals do not only consider the privacy-related attributes of applications, devices or services in the abstract. Rather, their decisions are heavily influenced by their pre-existing perceptions of, and relationships with, the companies behind such apps, devices and services. In particular, perceptions about a company's size, level of regulatory scrutiny, relationships with third parties, and pre-existing data exposure lead some users to choose an option which might otherwise appear worse from a privacy perspective. This finding suggests a need for tools that support users to incorporate these existing perceptions and relationships into their privacy-related decision making. 

Most users of smartphone apps remain unaware of what data about them is being collected, by whom, and how these data are being used. In this mixed methods investigation, we examine the question of whether revealing key data collection practices of smartphone apps may help people make more informed privacyrelated decisions. To investigate this question, we designed and prototyped a new class of privacy indicators, called Data Controller Indicators (DCIs), that expose previously hidden information flows out of the apps. Our lab study of DCIs suggests that such indicators do support people in making more confident and consistent choices, informed by a more diverse range of factors, including the number and nature of third-party companies that access users’ data. Furthermore, personalised DCIs, which are contextualised against the other apps an individual already uses, enable them to reason effectively about the differential impacts on their overall information exposure.