Governance and resilience as entry points for transforming food systems in the countdown to 2030.

Due to complex interactions, changes in any one area of food systems are likely to impact-and possibly depend on-changes in other areas. Here we present the first annual monitoring update of the indicator framework proposed by the Food Systems Countdown Initiative, with new qualitative analysis elucidating interactions across indicators. Since 2000, we find that 20 of 42 indicators with time series have been trending in a desirable direction, indicating modest positive change.

Differential Impact of Parental Practices and Parental Emotional Clarity on Child Symptoms in Single-Child vs. Multiple-Child Divorced Families.

Objective: The main objective of this study was to analyze the differences in parental emotional clarity and parental practices among families with a single child and families with more than one child, and their relationship with the children's internalizing and externalizing symptomatology, specifically, anxiety-depression and aggressive behavior in a conflictive divorce context.

Methods: The participants were 247 Spanish divorced parents. In total, 62% of the participants reported being the parents of one child and 38% of two children.

Degrowth as a plausible pathway for food systems transformation.

Food systems require urgent transformation towards social and ecological sustainability. Degrowth posits a radical pathway of transformation to reduce ecological impacts while increasing well-being and reducing inequality. Here we highlight that degrowth and food systems-albeit both linked to transformation-are not well integrated.

Identification of Predictors of Sarcopenia in Older Adults Using Machine Learning: English Longitudinal Study of Ageing.

After its introduction in the ICD-10-CM in 2016, sarcopenia is a condition widely considered to be a medical disease with important consequences for the elderly. Considering its high prevalence in older adults and its detrimental effects on health, it is essential to identify its risk factors to inform targeted interventions. Taking data from wave 2 of the ELSA, using ML-based methods, this study investigates which factors are significantly associated with sarcopenia.