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Unresolved states of mind regarding experiences of loss/abuse (U/d) are identified through lapses in the monitoring of reasoning, discourse, and behavior surrounding loss/abuse in response to the Adult Attachment Interview. Although the coding system for U/d has been widely used for decades, the individual indicators of unresolved loss/abuse have not been validated independently of the development sample. This study examined the psychometric validity of U/d, using individual participant data from 1,009 parent–child dyads across 13 studies. A latent class analysis showed that subsets of commonly occurring U/d indicators could differentiate interviewees with or without unresolved loss/abuse. Predictive models suggested a psychometric model of U/d consisting of a combination of these common indicators, with disbelief and psychologically confused statements regarding loss being especially important indicators of U/d. This model weakly predicted infant disorganized attachment. Multilevel regression analysis showed no significant association between ratings of unresolved other trauma and infant disorganized attachment, over and above ratings of unresolved loss/abuse. Altogether, these findings suggest that the coding system of U/d may have been overfitted to the initial development sample. Directions for further articulation and optimization of U/d are provided.

Although immunotherapy (IO) has changed the paradigm for the treatment of patients with advanced non-small cell lung cancers (aNSCLC), only around 30% to 50% of treated patients experience a long-term benefit from IO. Furthermore, the identification of the 30 to 50% of patients who respond remains a major challenge, as programmed Death-Ligand 1 (PD-L1) is currently the only biomarker used to predict the outcome of IO in NSCLC patients despite its limited efficacy. Considering the dynamic complexity of the immune system-tumor microenvironment (TME) and its interaction with the host's and patient's behavior, it is unlikely that a single biomarker will accurately predict a patient's outcomes. In this scenario, Artificial Intelligence (AI) and Machine Learning (ML) are becoming essential to the development of powerful decision-making tools that are able to deal with this high-complexity and provide individualized predictions to better match treatments to individual patients and thus improve patient outcomes and reduce the economic burden of aNSCLC on healthcare systems. I3LUNG is an international, multicenter, retrospective and prospective, observational study of patients with aNSCLC treated with IO, entirely funded by European Union (EU) under the Horizon 2020 (H2020) program. Using AI-based tools, the aim of this study is to promote individualized treatment in aNSCLC, with the goals of improving survival and quality of life, minimizing or preventing undue toxicity and promoting efficient resource allocation. The final objective of the project is the construction of a novel, integrated, AI-assisted data storage and elaboration platform to guide IO administration in aNSCLC, ensuring easy access and cost-effective use by healthcare providers and patients.

Childhood adversity is associated with higher adult weight, but few investigations prospectively test mechanisms accounting for this association. Using two socioeconomically high-risk prospective longitudinal investigations, the Minnesota Longitudinal Study of Risk and Adaptation (MLSRA; N = 267; 45.3% female) and the Fragile Families and Child Wellbeing Study (FFCWS; n = 2,587; 48.5% female), pathways between childhood adversity and later body mass index (BMI) were tested using impulsivity, emotion dysregulation, and overeating as mediators. Childhood adversity from 0 to 5 years included four types of adversities: greater unpredictability, threat/abuse, deprivation/neglect, and low socioeconomic status. Parents reported on child impulsivity, emotion dysregulation, and overeating. Height and weight were self-reported and measured at 32 and 37 years in MLSRA and at 15 years in FFCWS. FFCWS results indicated that threat, deprivation, and low socioeconomic status predicted greater impulsivity and emotion dysregulation at 5 years, which in turn predicted greater overeating at 9 years and higher BMI z-score at 15 years. Early unpredictability in FFCWS predicted higher BMI through greater impulsivity but not emotion dysregulation at age 5. MLSRA regression results replicated the threat/abuse → emotion dysregulation → overeating → higher BMI pathway. These findings suggest that different dimensions of early adversity may follow both similar and unique pathways to predict BMI.

In the present study, the effect of graphite lubricant additives on the dynamics of a single drop impact onto a heated surface has been investigated in the nucleate boiling and thermal atomization regimes.

To ensure safe flight under snowy conditions, aircraft manufacturers must demonstrate that each engine and its air inlet system can operate throughout the flight power range of the engine (including idling) in both falling and blowing snow conditions. This study is part of an effort to develop models for snow accretion. The focus here is on the modeling of snowflake transport by the carrier airflow and more specifically on the modeling of the drag coefficient of snowflakes. Based on comparisons between numerical and experimental results, we show that, provided that the complex shape of the snowflakes is accounted for, the models initially developed for ice crystal icing can also be used to estimate the drag coefficient of a snowflake. We also propose a methodology to estimate the geometric parameters of the drag models from two-dimensional images of the particles, which in general are the only available experimental data.

Individual differences in the quality of early experiences with primary caregivers have been reliably implicated in the development of socioemotional adjustment and, more recently, physical health. However, few studies have examined the development of such associations with physical health into the adult years. To that end, the current study used prospective, longitudinal data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (