Third-Party Effects

Most theories about effects of social embeddedness on trust define mechanisms that assume someone’s decision to trust is based on the reputation of the person to be trusted or on other available information. However, there is little empirical evidence about how subjects use the information that is available to them. In this chapter, we derive hypotheses about the effects of reputation and other information on trust from a range of theories and we devise an experiment that allows for testing these hypotheses simultaneously. We focus on the following mechanisms: learning, imitation, social comparison, and control. The results show that actors learn particularly from their own past experiences. Considering third-party information, imitation seems to be especially important

Population disruption: observational study of changes in the population distribution of the UK during the COVID-19 pandemic.

BACKGROUND: Mobility data have demonstrated major changes in human movement patterns in response to COVID-19 and associated interventions in many countries. This involves sub-national redistribution, short-term relocations, and international migration. Aggregated mobile phone location data combined with small-area census population data allow changes in the population distribution of the UK to be quantified with high spatial and temporal granularity. METHODS: In this paper, we combine detailed data from Facebook, measuring the location of approximately 6 million daily active Facebook users in 5km2 tiles in the UK with census-derived population estimates to measure population mobility and redistribution. We provide time-varying population estimates and assess spatial population changes with respect to population density and four key reference dates in 2020 (first UK lockdown, end of term, beginning of term, Christmas). RESULTS: We show how population estimates derived from Facebook data vary compared to mid-2020 small area population estimates by UK national statistics agencies. We also estimate that between March 2020 and March 2021, the total population of the UK declined and we identify important spatial variations in this population change, showing that low-density areas have experienced lower population decreases than urban areas. We estimate that, for the top 10% highest population tiles, the population has decreased by 6.6%. Finally, we provide evidence that geographic redistributions of population within the UK coincide with dates of non-pharmaceutical interventions including lockdowns and movement restrictions, as well as seasonal patterns of migration around holiday dates. CONCLUSIONS: The methods used in this study reveal significant changes in population distribution at high spatial and temporal resolutions that have not previously been quantified by available demographic surveys in the UK. We found early indicators of potential longer-term changes in the population distribution of the UK although it is not clear if these changes will persist after the COVID-19 pandemic

A Genetic Risk Score Distinguishes Different Types of Autoantibody-Mediated Membranous Nephropathy

INTRODUCTION: Membranous nephropathy (MN) is the leading cause of nephrotic syndrome in adults and is characterized by detectable autoantibodies against glomerular antigens, most commonly phospholipase A2 receptor 1 (PLA2R1) and thrombospondin type-1 domain containing 7A (THSD7A). In Europeans, genetic variation in at least five loci, PLA2R1, HLA-DRB1, HLA-DQA1, IRF4, and NFKB1, affects the risk of disease. Here, we investigated the genetic risk differences between different autoantibody states. METHODS: 1,409 MN individuals were genotyped genome-wide with a dense SNV array. The genetic risk score (GRS) was calculated utilizing the previously identified European MN loci, and results were compared with 4,929 healthy controls and 422 individuals with steroid-sensitive nephrotic syndrome. RESULTS: GRS was calculated in the 759 MN individuals in whom antibody status was known. The GRS for MN was elevated in the anti-PLA2R1 antibody-positive (N = 372) compared with both the unaffected control (N = 4,929) and anti-THSD7A-positive (N = 31) groups (p < 0.0001 for both comparisons), suggesting that this GRS reflects anti-PLA2R1 MN. Among PLA2R1-positive patients, GRS was inversely correlated with age of disease onset (p = 0.009). Further, the GRS in the dual antibody-negative group (N = 355) was intermediate between controls and the PLA2R1-positive group (p < 0.0001). CONCLUSION: We demonstrate that the genetic risk factors for PLA2R1- and THSD7A-antibody-associated MN are different. A higher GRS is associated with younger age of onset of disease. Further, a proportion of antibody-negative MN cases have an elevated GRS similar to PLA2R1-positive disease. This suggests that in some individuals with negative serology the disease is driven by autoimmunity against PLA2R1

Detecting behavioural changes in human movement to inform the spatial scale of interventions against COVID-19.

On March 23 2020, the UK enacted an intensive, nationwide lockdown to mitigate transmission of COVID-19. As restrictions began to ease, more localized interventions were used to target resurgences in transmission. Understanding the spatial scale of networks of human interaction, and how these networks change over time, is critical to targeting interventions at the most at-risk areas without unnecessarily restricting areas at low risk of resurgence. We use detailed human mobility data aggregated from Facebook users to determine how the spatially-explicit network of movements changed before and during the lockdown period, in response to the easing of restrictions, and to the introduction of locally-targeted interventions. We also apply community detection techniques to the weighted, directed network of movements to identify geographically-explicit movement communities and measure the evolution of these community structures through time. We found that the mobility network became more sparse and the number of mobility communities decreased under the national lockdown, a change that disproportionately affected long distance connections central to the mobility network. We also found that the community structure of areas in which locally-targeted interventions were implemented following epidemic resurgence did not show reorganization of community structure but did show small decreases in indicators of travel outside of local areas. We propose that communities detected using Facebook or other mobility data be used to assess the impact of spatially-targeted restrictions and may inform policymakers about the spatial extent of human movement patterns in the UK. These data are available in near real-time, allowing quantification of changes in the distribution of the population across the UK, as well as changes in travel patterns to inform our understanding of the impact of geographically-targeted interventions

Detecting behavioural changes in human movement to inform the spatial scale of interventions against COVID-19

BackgroundIn 2020, the UK enacted an intensive, nationwide lockdown on March 23 to mitigate transmission of COVID-19. As restrictions began to ease, resurgences in transmission were targeted by geographically-limited interventions of various stringencies. Understanding the spatial scale of networks of human interaction, and how these networks change over time, is critical to inform interventions targeted at the most at-risk areas without unnecessarily restricting areas at low risk of resurgence.MethodsWe use detailed human mobility data aggregated from Facebook users to determine how the spatially-explicit network of movements changed before and during the lockdown period, in response to the easing of restrictions, and to the introduction of locally-targeted interventions. We also apply community detection techniques to the weighted, directed network of movements to identify geographically-explicit movement communities and measure the evolution of these community structures through time.FindingsWe found that the mobility network became more sparse and the number of mobility communities decreased under the national lockdown, a change that disproportionately affected long distance journeys central to the mobility network. We also found that the community structure of areas in which locally-targeted interventions were implemented following epidemic resurgence did not show reorganization of community structure but did show small decreases in indicators of travel outside of local areas.InterpretationWe propose that communities detected using Facebook or other mobility data be used to assess the impact of spatially-targeted restrictions and may inform policymakers about the spatial extent of human movement patterns in the UK. These data are available in near real-time, allowing quantification of changes in the distribution of the population across the UK, as well as changes in travel patterns to inform our understanding of the impact of geographically-targeted interventions.Putting Research Into ContextEvidence before this studyLarge-scale intensive interventions in response to the COVID-19 pandemic have been implemented globally, significantly affecting human movement patterns. Mobility data show spatially-explicit network structure, but it is not clear how that structure changed in response to national or locally-targeted interventions.Added value of this studyWe used daily mobility data aggregated from Facebook users to quantify changes in the travel network in the UK during the national lockdown, and in response to local interventions. We identified changes in human behaviour in response to interventions and identified the community structure inherent in these networks. This approach to understanding changes in the travel network can help quantify the extent of strongly connected communities of interaction and their relationship to the extent of spatially-explicit interventions.Implications of all the available evidenceWe show that spatial mobility data available in near real-time can give information on connectivity that can be used to understand the impact of geographically-targeted interventions and in the future, to inform spatially-targeted intervention strategies.Data SharingData used in this study are available from the Facebook Data for Good Partner Program by application. Code and supplementary information for this paper are available online (https://github.com/hamishgibbs/facebook_mobility_uk), alongside publication.</jats:sec

Analysis of Temperature-to-Polarization Leakage in BICEP3 and Keck CMB Data from 2016 to 2018

The Bicep/Keck Array experiment is a series of small-aperture refracting telescopes observing degree-scale Cosmic Microwave Background polarization from the South Pole in search of a primordial B-mode signature. As a pair differencing experiment, an important systematic that must be controlled is the differential beam response between the co-located, orthogonally polarized detectors. We use high-fidelity, in-situ measurements of the beam response to estimate the temperature-to-polarization (T → P) leakage in our latest data including observations from 2016 through 2018. This includes three years of Bicep3 observing at 95 GHz, and multifrequency data from Keck Array. Here we present band-averaged far-field beam maps, differential beam mismatch, and residual beam power (after filtering out the leading difference modes via deprojection) for these receivers. We show preliminary results of "beam map simulations," which use these beam maps to observe a simulated temperature (no Q/U) sky to estimate T → P leakage in our real data

The genetic architecture of membranous nephropathy and its potential to improve non-invasive diagnosis

Membranous Nephropathy (MN) is a rare autoimmune cause of kidney failure. Here we report a genome-wide association study (GWAS) for primary MN in 3,782 cases and 9,038 controls of East Asian and European ancestries. We discover two previously unreported loci, NFKB1 (rs230540, OR = 1.25, P = 3.4 × 10-12) and IRF4 (rs9405192, OR = 1.29, P = 1.4 × 10-14), fine-map the PLA2R1 locus (rs17831251, OR = 2.25, P = 4.7 × 10-103) and report ancestry-specific effects of three classical HLA alleles: DRB1*1501 in East Asians (OR = 3.81, P = 2.0 × 10-49), DQA1*0501 in Europeans (OR = 2.88, P = 5.7 × 10-93), and DRB1*0301 in both ethnicities (OR = 3.50, P = 9.2 × 10-23 and OR = 3.39, P = 5.2 × 10-82, respectively). GWAS loci explain 32% of disease risk in East Asians and 25% in Europeans, and correctly re-classify 20-37% of the cases in validation cohorts that are antibody-negative by the serum anti-PLA2R ELISA diagnostic test. Our findings highlight an unusual genetic architecture of MN, with four loci and their interactions accounting for nearly one-third of the disease risk