Foreground modelling via Gaussian process regression: An application to HERA data

The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in $\sim 2$ hours of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an "intrinsic" and instrumentally corrupted component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating the line of sight power spectrum over scales $k_{\parallel} \le 0.2$ h cMpc$^{-1}$) and a baseline dependent periodic signal with a period of $\sim 1$ MHz (dominating over $k_{\parallel} \sim 0.4 - 0.8$h cMpc$^{-1}$) which should be distinguishable from the 21-cm EoR signal whose typical coherence-scales is $\sim 0.8$ MHz

Foreground modelling via Gaussian process regression: An application to HERA data

The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in $\sim 2$ hours of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an "intrinsic" and instrumentally corrupted component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating the line of sight power spectrum over scales $k_{\parallel} \le 0.2$ h cMpc$^{-1}$) and a baseline dependent periodic signal with a period of $\sim 1$ MHz (dominating over $k_{\parallel} \sim 0.4 - 0.8$h cMpc$^{-1}$) which should be distinguishable from the 21-cm EoR signal whose typical coherence-scales is $\sim 0.8$ MHz

Stress and subjective well-being among first year UK undergraduate students

Transition to university is stressful and successful adjustment is imperative for well-being. Historically research on transitional stress focussed on negative outcomes and ill health. This is the first UK study applying a positive psychology approach to investigate the characteristics that facilitate adjustment among new university students. A range of psychological strengths conceptualised as covitality factors, shown individually to influence the stress and subjective well-being (SWB) relationship were assessed among 192 first year UK undergraduates in week three of their first semester and again six months later. Path analyses revealed that optimism mediated the relationship between stress and negative affect (a component of SWB) over time, and academic self-efficacy demonstrated significant relationships with life satisfaction and positive affect. Contrary to predictions, stress levels remained stable over time although academic alienation increased and self-efficacy decreased. Optimism emerged as a key factor for new students to adjust to university, helping to buffer the impact of stress on well-being throughout the academic year. Incorporating stress management and psycho-educational interventions to develop strengths is discussed as a way of promoting confidence and agency in new students to help them cope better with the stress at university

Impaired mitochondrial calcium efflux contributes to disease progression in models of Alzheimer’s disease

Impairments in neuronal intracellular calcium (iCa2+) handling may contribute to Alzheimer’s disease (AD) development. Metabolic dysfunction and progressive neuronal loss are associated with AD progression, and mitochondrial calcium (mCa2+) signaling is a key regulator of both of these processes. Here, we report remodeling of the mCa2+ exchange machinery in the prefrontal cortex of individuals with AD. In the 3xTg-AD mouse model impaired mCa2+ efflux capacity precedes neuropathology. Neuronal deletion of the mitochondrial Na+/Ca2+ exchanger (NCLX, Slc8b1 gene) accelerated memory decline and increased amyloidosis and tau pathology. Further, genetic rescue of neuronal NCLX in 3xTg-AD mice is sufficient to impede AD-associated pathology and memory loss. We show that mCa2+ overload contributes to AD progression by promoting superoxide generation, metabolic dysfunction and neuronal cell death. These results provide a link between the calcium dysregulation and metabolic dysfunction hypotheses of AD and suggest mCa2+ exchange as potential therapeutic target in AD