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Scholars have acknowledged the importance of green human resource management (HRM). Green HRM has been considered a vital mechanism for companies to move towards a sustainable organisation. However, the factors that drive green HRM have not been thoroughly investigated. This study investigated how green HRM interacts with the linkages among relative advantage, stakeholder pressure, and top management commitment based on the Stakeholder Theory and Resource‐based view theory. Data were collected from 112 large manufacturing firms using a survey. Partial least squares modelling was used to test the hypotheses in the research. All hypotheses were accepted except for relative advantage concerning green analysis and description of job positions, green recruitment and green rewards. Overall, it offers guidelines for decision makers on how to emphasise these factors in organisations and subsequently create a culture of environmentally friendly organisations particularly from the perspective green HRM. [ABSTRACT FROM AUTHOR]

A transition to the circular economy may be a solution to dissociate financial growth from negative environmental impacts. However, this transition can require an in‐depth understanding of which enablers an organization requires. This research aims to identify the enablers of the circular economy presented in the literature and analyze them against the theories about resources, capabilities, and dynamic capabilities and against the ReSOLVE framework. Through a systematic literature review, this research identified thirteen enablers grouped in three different dimensions (management and people; structure, product, and process; and relationship with stakeholders). Some enablers can be considered as resources, capabilities, or dynamic capabilities and are capable of helping firms to reach a successful transition to the circularity. They may even be sources of competitive advantage in the context of circular economy, mainly the enablers of the dimension "structure, product, and process." The dimensions of enablers have direct and indirect links with the ReSOLVE framework action areas. Through the discussions of some real cases, it was possible to exemplify how some enablers lead companies to circularity. Besides that, this research posits seven research gaps for future studies. [ABSTRACT FROM AUTHOR]

Background: New methods have recently been developed to model longitudinal amyloid biomarkers in AD. This work compares three methods in three different cohorts, and investigates associations of APOE‐e4 status with amyloid onset age and amyloid accumulation rates. Method: Participants from ADNI (N=1213), BLSA (N=205) and WRAP (N=272) with available amyloid PET imaging and APOE genotype were included in the study (Table 1). Amyloid (A) burden was quantified using established methods (ADNI: BAI processed SUVR, WM reference region; BLSA: SRTM‐LRSC DVR, PVC, cerebellum GM reference region; WRAP: graphical analysis DVR, cerebellum GM reference region). Group‐based trajectory modeling (GBTM), ordinary differential equation‐Gaussian process (ODE‐GP) and sampled iterative local linear approximation (SILLA) methods were used to model longitudinal amyloid accumulation, estimate A+ onset age, and align amyloid values by years A+ for each cohort. Forward and backward SUVR/DVR prediction, estimated age of A+ onset, and associations of DVR/SUVR residuals with age, time from reference scan, and SUVR/DVR, were compared for each model and cohort. The impact of APOE genotype on A+ onset and amyloid PET accumulation trajectories was investigated using discrete rate sample, Kaplan‐Meier curves, and accelerated failure time models. Result: All methods produced similar amyloid vs. years A+ curves for each cohort (Figure 1) with most notable discrepancies occurring in the A‐ range. DVR/SUVR prediction performance was similar across methods and was better for retrospective vs. prospective prediction. Amyloid accumulation rates and trajectories were similar for APOE‐e4+ and APOE‐e4‐ genotypes (Figure 2). Kaplan‐Meier curves were different between the APOE groups (log‐rank test, p<0.005 for each test; Figure 3), with the e4+ group exhibiting earlier A+ onset ages compared to e4‐ (Table 2) for all methods and cohorts. Conclusion: This work demonstrates similar performance of three novel methods for modeling longitudinal amyloid PET burden and estimating individualized A+ onset age. Results suggest that APOE‐e4 status affects the A+ onset age, but not the rate of amyloid PET accumulation. Ongoing work is investigating potential differences in amyloid accumulation trajectories and A+ onset age between APOE genotypes (e.g. e3e3, e3e4, e4e4,...) and is investigating relationships between A+ onset, APOE genotype and onset of cognitive impairment. [ABSTRACT FROM AUTHOR]

Background: The analysis of the longitudinal progression of plasma biomarkers and amyloid imaging might lead to new insights in the early pattern of biomarker changes. Method: Plasma Aβ42/Aβ40, pTau231/Aβ42, pTau181/Aβ42, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NFL) were measured for n = 223 subjects and 1673 total visits in the Wisconsin Registry for Alzheimer's Prevention (WRAP) using Quanterix Simoa assays at the University of Gothenburg. Global distribution volume ratio (DVR) was computed from 11C‐Pittsburgh compound B PET scans. Inclusion criteria included Amyloid or CSF Amyloid positive status, and at least one plasma serial value. Trajectories spanning age 46.9 to 80.3 years old for the DVR and plasma biomarkers were estimated by fitting a vector field to the longitudinal data (https://arxiv.org/abs/2206.15215). A Louvain community clustering analysis was performed. The time from Amyloid positivity (DVR = 1.19; centiloid of 22) was also computed for all individual trajectories that crossed this threshold. Result: The Louvain community clustering algorithm resulted in three distinct communities (Fig 1): no increase in DVR or plasma biomarkers (Green, n = 88(39.5%)); increase in DVR and all plasma biomarkers except NFL (Red, 41(18.4%)); and mixed DVR plus GFAP and NFL increase (Blue, 94(42.2%)). Time since Amyloid positivity (TSAP) was computed for the subset of subjects (n = 84) expected to cross the Amyloid positivity threshold. The Louvain communities as a function of TSAP are shown in Fig 2. Conclusion: We identified three communities regarding the longitudinal progression of amyloid PET and plasma biomarker levels. Plasma biomarkers were dynamic up to 30 years before Amyloid positivity. [ABSTRACT FROM AUTHOR]

Background: High‐frequency biomarkers (HFB) are measurements collected weekly, on a daily basis or even more frequently. They are designed to detect early progression from one cognitive state to a later stage. Examples include home‐based measurements, e.g., computer usage and sleep patterns. In randomized controlled trials (RCT), we aim to detect the difference in trajectories of HFB between placebo and experimental group. Current statistical analysis e.g. linear mixed effect with random intercept models detect changes in the mean trajectory but fail to detect changes in the covariance structure, thus requiring longer trials. We examined whether more careful modeling, using historical data, would reduce the sample size needed for a RCT? Objective: (1) to present a novel statistical methodology for analyzing RCTs whose outcomes are HFB based on historical data of cognitively normal subjects, and (2) to evaluate this methodology using simulated data first, then using data from the Oregon Center for Aging and Technology (ORCATECH). Methods: The statistical methodology proposed proceeds in three phases (Figure 1). Highlights include the use of historical data for cognitively normal subjects and the use of a Gaussian process with parameterized mean function and covariance structure. Examples of results from simulated data are shown in Figure 2, demonstrating the superior power of the methodology compared to the linear mixed‐effects model. ORCATECH's weekly computer usage data was used (Figure 3). We computed an RCT effect size with 86 cognitively normal subjects and 11 subjects who were cognitively normal at baseline, but would eventually develop MCI within 139 weeks on average (sd=72). Results: Using this new approach, we found an effect size of W=0.616 (Area Under the curve), outperforming a baseline method which yielded an effect size of W=0.515. Respective total sample sizes for an RCT with alpha=0.05 and power=0.8 are 74 and 1028, for the new versus the conventional approach, respectively. Conclusions: We have shown that a careful statistical procedure allows for maximally benefiting from HFB by reducing the sample size needed for a 100 week RCT. More experiments will be necessary to validate these findings. [ABSTRACT FROM AUTHOR]

Background: Accumulation of beta‐amyloid plaques, an early hallmark neuropathology of Alzheimer's disease (AD), begin more than a decade prior to the onset of clinical symptoms. Understanding the temporal progression of early amyloid accumulation is important for predicting who will become amyloid‐positive and when. Based on observations from several longitudinal amyloid positron emission tomography (PET) studies that the rate of overall amyloid burden is closely associated with baseline level of amyloid, we develop a formal mathematical model capturing this phenomenon and use it to (i) align temporal trajectories of amyloid accumulation across individuals, (ii) create a fine‐grained timeline of amyloid accumulation, and (iii) estimate the age at onset of amyloid positivity per individual. Method: We used (PiB) PET scans from the Wisconsin Registry for Alzheimer's Prevention (WRAP) and the Baltimore Longitudinal Study of Aging (BLSA) to assess global amyloid burden cDVR, see Figure 1. A first‐order autonomous model for the slope of cDVR as a function of cDVR was fit to the data. We used Bayesian modeling with a Gaussian process (GP) prior and independent homoscedastic noise. Result: The best fit was obtained with a quadratic GP kernel (Figure 2). Since the system is autonomous, the trajectories are parallel and thus can be registered by translation. The progression of the Amyloid burden is thus visualized as a function of the time (in years) from PIB positivity in Figure 3. The estimated age when the PIB cDVR becomes positive is estimated. This age depends on APOE status, with a significant difference between the APOE groups [3,3] and [3,4]. p‐value=.008 in WRAP, p‐value=.054 In BLSA. Conclusion: Large studies on aging allow studying the time‐progression of the amyloid burden. We found that an autonomous ordinary differential equation allows for a good explanatory model of the PIB‐DVR data in the WRAP as well as in the BLSA studies. Still, a few participants do not fit the data closely. It remains to understand if this is due to noisy measurements or to specific conditions. Also, alternative dynamical systems, including Hamiltonian, still need to be investigated and compared. [ABSTRACT FROM AUTHOR]