The Metabolic Signature of Cardiorespiratory Fitness: A Systematic Review

Background Cardiorespiratory fitness (CRF) is a potent health marker, the improvement of which is associated with a reduced incidence of non-communicable diseases and all-cause mortality. Identifying metabolic signatures associated with CRF could reveal how CRF fosters human health and lead to the development of novel health-monitoring strategies. Objective This article systematically reviewed reported associations between CRF and metabolites measured in human tissues and body fluids. Methods PubMed, EMBASE, and Web of Science were searched from database inception to 3 June, 2021. Metabolomics studies reporting metabolites associated with CRF, measured by means of cardiopulmonary exercise test, were deemed eligible. Backward and forward citation tracking on eligible records were used to complement the results of database searching. Risk of bias at the study level was assessed using QUADOMICS. Results Twenty-two studies were included and 667 metabolites, measured in plasma (n = 619), serum (n = 18), skeletal muscle (n = 16), urine (n = 11), or sweat (n = 3), were identified. Lipids were the metabolites most commonly positively (n = 174) and negatively (n = 274) associated with CRF. Specific circulating glycerophospholipids (n = 85) and cholesterol esters (n = 17) were positively associated with CRF, while circulating glycerolipids (n = 152), glycerophospholipids (n = 42), acylcarnitines (n = 14), and ceramides (n = 12) were negatively associated with CRF. Interestingly, muscle acylcarnitines were positively correlated with CRF (n = 15). Conclusions Cardiorespiratory fitness was associated with circulating and muscle lipidome composition. Causality of the revealed associations at the molecular species level remains to be investigated further. Finally, included studies were heterogeneous in terms of participants’ characteristics and analytical and statistical approaches. PROSPERO Registration Number CRD42020214375.

Psychological Adjustment to Spousal Bereavement in Older Adults: A Systematic Review

Research articles examining psychological adjustment to spousal bereavement in older adults (65+) were identified through searches on five electronical databases alongside forward citation and reference list searches. A total of 15 articles involving 686 unique participants were identified. Five characteristics were discovered which can facilitate and inhibit psychological adjustment to spousal bereavement in older adults: the pre-loss spousal relationship, social support, finding meaning and spirituality in loss, the surviving spouse’s personality traits, and death characteristics. These findings support that concepts of ‘meaning making’ and social support should be incorporated into therapeutic work with bereaved spouses to help facilitate psychological adjustment.

Patent Valuation Using Citations: A Review and Sensitivity Analysis

As a result of studies demonstrating a correlation between a patent’s value and its forward citation count, patent valuation using forward citations has been increasingly used by practitioners when a patent’s value has not been otherwise established. Although potential limitations of patent citation analysis have been discussed in the past, there is little empirical research demonstrating the sensitivity of estimated patent values to various assumptions embedded within the method. We first summarize an approach that has been used by prior practitioners to estimate the relative value of patents within a portfolio using forward citations, and then perform various analyses to investigate the sensitivity of the approach to certain assumptions. We find that some concerns of prior literature are well-founded, while others are less so. For example, we confirm that biased valuations will result from failure to properly control for patent age and technology. Our analysis also finds that truncation bias is a problem when analyzing recently issued patents, which confirms findings from existing literature. We estimate the rate at which such truncation bias dissipates as patents age and find that the bias for the median patent is reduced to below 10% within five years from the date of publication, although additional variation can remain on an individualized level. Regarding the issue of self-citations, we find that the valuation approach using forward citation analysis can be (but is not always) sensitive to the issue of self-citations, with a median difference of 16.8%. Finally, the valuation approach using forward citation analysis appears to be robust to assumptions underlying patent cohort construction.

Evidencia epidemiológica acerca del rol de la hostelería en la transmisión de la COVID-19: una revisión rápida de la literatura

Objective: To review the scientific epidemiologic evidence on the role of hospitality venues in the incidence or mortality from COVID-19. Methods: We included studies conducted in any population, describing either the impact of the closure or reopening of hospitality venues, or exposure to these venues, on the incidence or mortality from COVID-19. We used a snowball sampling approach with backward and forward citation search along with co-citations. Results: We found 20 articles examining the role of hospitality venues in the epidemiology of COVID-19. Modeling studies showed that interventions reducing social contacts in indoor venues can reduce COVID-19 transmission. Studies using statistical models showed similar results, including that the closure of hospitality venues is amongst the most effective measures in reducing incidence or mortality. Case studies highlighted the role of hospitality venues in generating super-spreading events, along with the importance of air flow and ventilation inside these venues. Conclusions: We found consistent results across studies showing that the closure of hospitality venues is amongst the most effective measures to reduce the impact of COVID-19. We also found support for measures limiting capacity and improving ventilation to consider during the re-opening of these venues.

Search for the Developing Trends by Patent Analysis: A Case Study of Lithium-Ion Battery Electrolytes

Lithium-ion batteries (LIBs) are now used in electric vehicles (EVs), and the electrolyte is one of the major components governing the performance of LIBs. The patent count-based method or patent indicator was used to understand the development status of the specific technology field. However, these approaches cannot provide a complete picture to realize the technology development. Therefore, the goal of this work is to develop a holistic approach to identify technological development trends. The top six patent assignees are first selected by an issued patent counts analysis, including Ube, Mitsubishi Chem., Panasonic, Sony, LG Chem., and Samsung SDI. The “modified” Ernst indicators are applied to reflect different innovation strategies among these patent assignees. The forward-citation analysis and technology-function matrix show that using mixed lithium salts and organic solvents with novel additives compounds are the developing trends to improve the performance of LIBs. The multi-dimensional scaling shows technological similarities among these six companies. The developed approaches can be used to obtain a better overview for electrolyte technology and be also applicable to other technological fields.

Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks

Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional intensity of previously received citations. Recent approaches model the conditional intensity as a chain of recurrent neural networks to capture memory dependency in hopes of reducing the restrictions of the parametric form of the intensity function. For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent. In this paper, we propose a sequence-to-sequence model which employs an attention-of-attention mechanism to capture the dependencies of these multiple time sequences. Furthermore, the proposed model is able to forecast both the timestamp and the category of a patent's next citation. Extensive experiments on a large patent citation dataset collected from USPTO demonstrate that the proposed model outperforms state-of-the-art models at forward citation forecasting.