The Influence of Age-Related Differences in Prior Knowledge and Attentional Refreshing Opportunities on Episodic Memory

Objectives. The assumption that working memory (WM) is embedded within long-term memory suggests that the effectiveness of switching information between activated states in WM (i.e., attentional refreshing) may depend on whether that information is semantically relevant. Given that older adults often have greater general knowledge than younger adults, age-related deficits in episodic memory (EM) could be ameliorated by studying information that has existing semantic representations compared with unknown information. Method. Younger and older adults completed a modified operation span task that varied the number of refreshing opportunities. The memoranda used were equally known to younger and older adults (neutral words; e.g., father), better known to older adults than younger adults (dated words; e.g., mirth), or unknown to both groups (unknown words; e.g., cobot). Results. Results for immediate and delayed recall indicated an age-related improvement for dated memoranda and no age difference for unknown memoranda. Furthermore, refreshing opportunities predicted delayed recall of neutral memoranda more strongly for younger adults than older adults, whereas older adults' recall advantage for dated memoranda was explained by their prior knowledge and not refreshing opportunities. Discussion. The results suggest that older adults' EM deficits could potentially be ameliorated by incorporating their superior knowledge to supplement relatively ineffective attentional refreshing in W

The Influence of Age-Related Differences in Prior Knowledge and Attentional Refreshing Opportunities on Episodic Memory

Objectives. The assumption that working memory (WM) is embedded within long-term memory suggests that the effectiveness of switching information between activated states in WM (i.e., attentional refreshing) may depend on whether that information is semantically relevant. Given that older adults often have greater general knowledge than younger adults, age-related deficits in episodic memory (EM) could be ameliorated by studying information that has existing semantic representations compared with unknown information. Method. Younger and older adults completed a modified operation span task that varied the number of refreshing opportunities. The memoranda used were equally known to younger and older adults (neutral words; e.g., father), better known to older adults than younger adults (dated words; e.g., mirth), or unknown to both groups (unknown words; e.g., cobot). Results. Results for immediate and delayed recall indicated an age-related improvement for dated memoranda and no age difference for unknown memoranda. Furthermore, refreshing opportunities predicted delayed recall of neutral memoranda more strongly for younger adults than older adults, whereas older adults' recall advantage for dated memoranda was explained by their prior knowledge and not refreshing opportunities. Discussion. The results suggest that older adults' EM deficits could potentially be ameliorated by incorporating their superior knowledge to supplement relatively ineffective attentional refreshing in WM

Chaos, decoherence and quantum cosmology

In this topical review we discuss the connections between chaos, decoherence and quantum cosmology. We understand chaos as classical chaos in systems with a finite number of degrees of freedom, decoherence as environment induced decoherence and quantum cosmology as the theory of the Wheeler - DeWitt equation or else the consistent history formulation thereof, first in mini super spaces and later through its extension to midi super spaces. The overall conclusion is that consideration of decoherence is necessary (and probably sufficient) to sustain an interpretation of quantum cosmology based on the Wave function of the Universe adopting a Wentzel - Kramers - Brillouin form for large Universes, but a definitive account of the semiclassical transition in classically chaotic cosmological models is not available in the literature yet.Comment: 40 page

Estimating the Associated Burden of Illness and Healthcare Utilization of Newly Diagnosed Patients Aged ≥65 with Mantle Cell Lymphoma (MCL) in Ontario, Canada

Background: With the emergence of therapies for mantle cell lymphoma (MCL), understanding the treatment patterns and burden of illness among older patients with MCL in Canada is essential to inform decision making. Methods: A retrospective study using administrative data matched individuals aged ≥65 who were newly diagnosed with MCL between 1 January 2013 and 31 December 2016 with general population controls. Cases were followed for up to 3 years in order to assess healthcare resource utilization (HCRU), healthcare costs, time to next treatment or death (TTNTD), and overall survival (OS); all were stratified according to first-line treatment. Results: This study matched 159 MCL patients to 636 controls. Direct healthcare costs were highest among MCL patients in the first year following diagnosis (Y1: CAD 77,555 ± 40,789), decreased subsequently (Y2: CAD 40,093 ± 28,720; Y3: CAD 36,059 ± 36,303), and were consistently higher than the costs for controls. The 3-year OS after MCL diagnosis was 68.6%, with patients receiving bendamustine + rituximab (BR) experiencing a significantly higher OS compared to patients treated with other regimens (72.4% vs. 55.6%, p = 0.041). Approximately 40.9% of MCL patients initiated a second-line therapy or died within 3 years. Conclusion: Newly diagnosed MCL presents a substantial burden to the healthcare system, with almost half of all patients progressing to a second-line therapy or death within 3 years

Strategies to mitigate the impact of the COVID-19 pandemic on child and youth well-being: a scoping review protocol

Introduction Children and youth are often more vulnerable than adults to emotional impacts of trauma. Wide-ranging negative effects (eg, social isolation, lack of physical activity) of the COVID-19 pandemic on children and youth are well established. This scoping review will identify, describe and categorise strategies taken to mitigate potentially deleterious impacts of the COVID-19 pandemic on children, youth and their families.Methods and analysis We will conduct a scoping review following the Arksey-O’Malley five-stage scoping review method and the Scoping Review Methods Manual by the Joanna Briggs Institute. Well-being will be operationalised according to pre-established domains (health and nutrition, connectedness, safety and support, learning and competence, and agency and resilience). Articles in all languages for this review will be identified in CINAHL, Cochrane CENTRAL Register of Controlled Trials, EMBASE, ERIC, Education Research Complete, MEDLINE and APA PsycINFO. The search strategy will be restricted to articles published on or after 1 December 2019. We will include primary empirical and non-empirical methodologies, excluding protocols, reports, opinions and editorials, to identify new data for a broad range of strategies to mitigate potentially deleterious impacts of the COVID-19 pandemic on child and youth well-being. Two reviewers will calibrate screening criteria and the data abstraction form and will independently screen records and abstract data. Data synthesis will be performed according to the convergent integrated approach described by the Joanna Briggs Institute.Ethics and dissemination Ethical approval is not applicable as this review will be conducted on published data. Findings of this study will be disseminated at national and international conferences and will inform our pan-Canadian multidisciplinary team of researchers, public, health professionals and knowledge users to codesign and pilot test a digital psychoeducational health tool—an interactive, web-based tool to help Canadian youth and their families address poor mental well-being resulting from and persisting beyond the COVID-19 pandemic

A large, open source dataset of stroke anatomical brain images and manual lesion segmentations.

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods

Effectiveness of COVID-19 Vaccines in Preventing Hospitalization Among Adults Aged ≥65 Years - COVID-NET, 13 States, February-April 2021.

Clinical trials of COVID-19 vaccines currently authorized for emergency use in the United States (Pfizer-BioNTech, Moderna, and Janssen [Johnson & Johnson]) indicate that these vaccines have high efficacy against symptomatic disease, including moderate to severe illness (1-3). In addition to clinical trials, real-world assessments of COVID-19 vaccine effectiveness are critical in guiding vaccine policy and building vaccine confidence, particularly among populations at higher risk for more severe illness from COVID-19, including older adults. To determine the real-world effectiveness of the three currently authorized COVID-19 vaccines among persons aged ≥65 years during February 1-April 30, 2021, data on 7,280 patients from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) were analyzed with vaccination coverage data from state immunization information systems (IISs) for the COVID-NET catchment area (approximately 4.8 million persons). Among adults aged 65-74 years, effectiveness of full vaccination in preventing COVID-19-associated hospitalization was 96% (95% confidence interval [CI] = 94%-98%) for Pfizer-BioNTech, 96% (95% CI = 95%-98%) for Moderna, and 84% (95% CI = 64%-93%) for Janssen vaccine products. Effectiveness of full vaccination in preventing COVID-19-associated hospitalization among adults aged ≥75 years was 91% (95% CI = 87%-94%) for Pfizer-BioNTech, 96% (95% CI = 93%-98%) for Moderna, and 85% (95% CI = 72%-92%) for Janssen vaccine products. COVID-19 vaccines currently authorized in the United States are highly effective in preventing COVID-19-associated hospitalizations in older adults. In light of real-world data demonstrating high effectiveness of COVID-19 vaccines among older adults, efforts to increase vaccination coverage in this age group are critical to reducing the risk for COVID-19-related hospitalization

A large, open source dataset of stroke anatomical brain images and manual lesion segmentations

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods