A need for time-varying models to suppress artefacts of tACS in the M/EEG

Rhythmic modulation of brain activity by transcranial alternating current stimulation (tACS) can entrain neural oscillations in a frequency- and phase-specific manner. However, large stimulation artefacts contaminate concurrent 'online' neuroimaging measures, including magneto- and electro-encephalography (M/EEG) — restricting most analyses to periods free from stimulation ('offline' aftereffects). While many published methods exist for removing artefacts of tACS from M/EEG recordings, they universally assume linear artefacts: either time-invariance (i.e., an artefact is a scaled version of itself from cycle to cycle) or sensor-invariance (i.e., artefacts are scaled versions of one another from sensor to sensor). However, heartbeat and respiration both nonlinearly modulate the amplitude and phase of these artefacts, predominantly via changes in scalp impedance. The spectral symmetry this introduces to the M/EEG spectra may lead to false-positive evidence for entrainment around the frequency of tACS, if not adequately suppressed. Good electrophysiological evidence for entrainment therefore requires that tACS artefacts are fully accounted for before comparing online spectra to a control (e.g., as might be observed during sham stimulation). Here I outline an approach to linearly solve templates for tACS artefacts, and demonstrate how event-locked perturbations to amplitude and phase can be introduced from simultaneous recordings of heartbeat and respiration — effectively forming time-varying models of tACS artefacts. These models are constructed for individual sensors, and can therefore be used in contexts with few EEG sensors and with no assumption of artefact collinearity. I also discuss the feasibility of this approach in the absence of simultaneous recordings of heartbeat and respiration traces.

Association of lactate dehydrogenase with mortality in incident hemodialysis patients

Background Lactate dehydrogenase (LDH) plays a role in the glucose metabolism of the human body. Higher LDH levels have been linked to mortality in various cancer types; however, the relationship between LDH and survival in incident hemodialysis (HD) patients has not yet been examined. We hypothesized that higher LDH level is associated with higher death risk in these patients. Methods We examined the association of baseline and time-varying serum LDH with all-cause, cardiovascular and infection-related mortality among 109 632 adult incident HD patients receiving care from a large dialysis organization in the USA during January 2007 to December 2011. Baseline and time-varying survival models were adjusted for demographic variables and available clinical and laboratory surrogates of malnutrition–inflammation complex syndrome. Results There was a linear association between baseline serum LDH levels and all-cause, cardiovascular and infection-related mortality in both baseline and time-varying models, except for time-varying infection-related mortality. Adjustment for markers of inflammation and malnutrition attenuated the association in all models. In fully adjusted models, baseline LDH levels ≥360 U/L were associated with the highest risk of all-cause mortality (hazard ratios = 1.19, 95% confidence interval 1.14–1.25). In time-varying models, LDH >280 U/L was associated with higher death risk in all three hierarchical models for all-cause and cardiovascular mortality. Conclusions Higher LDH level >280 U/L was incrementally associated with higher all-cause and cardiovascular mortality in incident dialysis patients, whereas LDH <240 U/L was associated with better survival. These findings suggest that the assessment of metabolic functions and monitoring for comorbidities may confer survival benefit to dialysis patients.

A Survey on Nonstrategic Models of Opinion Dynamics

The paper presents a survey on selected models of opinion dynamics. Both discrete (more precisely, binary) opinion models as well as continuous opinion models are discussed. We focus on frameworks that assume non-Bayesian updating of opinions. In the survey, a special attention is paid to modeling nonconformity (in particular, anticonformity) behavior. For the case of opinions represented by a binary variable, we recall the threshold model, the voter and q-voter models, the majority rule model, and the aggregation framework. For the case of continuous opinions, we present the DeGroot model and some of its variations, time-varying models, and bounded confidence models.

Impact of time-varying productivity on estimated stock–recruitment parameters and biological reference points

Models with time-varying parameters are increasingly being considered in the assessment of fish stocks, but their reliability when used to derive biological reference points or benchmarks has not been thoroughly evaluated. Here, we evaluated stock–recruitment models with and without time-varying productivity in a simulation framework for sockeye salmon (Oncorhynchus nerka) under different scenarios of productivity and exploitation. Ignoring trends in productivity led to overestimates of productivity and underestimates of capacity when both exploitation rates and productivity declined over time, resulting in an underestimation on average of benchmarks of biological status. Despite being less biased, time-varying models had relatively poor fit based on AICc and BIC model selection criteria. Our simulation results were compared with empirical analyses of 12 Fraser River sockeye salmon stocks in British Columbia, Canada. Although benchmarks were less biased when based on time-varying models, underlying true benchmarks based on spawner abundances at maximum sustainable yield, SMSY, trend downwards when productivity declines, which may not be aligned with conservation objectives. We conclude with best practices when adapting biological benchmarks to time-varying productivity.

OpenQuake Implementation of the Canterbury Seismic Hazard Model

ABSTRACT This article describes the release of the GNS Science Canterbury Seismic Hazard Model (CSHM), as implemented in the Global Earthquake Model’s OpenQuake software. Time‐varying models are implemented for the 50 yr time period between 2014 and 2064, as well as the 1 yr period from 1 September 2018 to 31 August 2019. Previous implementations have been confined to GNS in‐house software, and although source model input files have been made publicly available, this implementation improves the levels of visibility, documentation, and version control. Because of practical constraints in preparing a model for routine analysis, some corrections and changes to the previous implementations have been made. These constraints highlight issues for consideration when developing future hazard models, particularly the necessity of maintaining a balance between best‐practice science and practical model implementation. By implementing the CSHM in OpenQuake, the model is now in a form that allows users to obtain model outputs for engineering design, risk analyses, and prospective model testing.