miRNA-126 Orchestrates an Oncogenic Program in B Cell Precursor Acute Lymphoblastic Leukemia

MicroRNA (miRNA)-126 is a known regulator of hematopoietic stem cell quiescence. We engineered murine hematopoiesis to express miRNA-126 across all differentiation stages. Thirty percent of mice developed monoclonal B cell leukemia, which was prevented or regressed when a tetracycline-repressible miRNA-126 cassette was switched off. Regression was accompanied by upregulation of cell-cycle regulators and B cell differentiation genes, and downregulation of oncogenic signaling pathways. Expression of dominant-negative p53 delayed blast clearance upon miRNA-126 switch-off, highlighting the relevance of p53 inhibition in miRNA-126 addiction. Forced miRNA-126 expression in mouse and human progenitors reduced p53 transcriptional activity through regulation of multiple p53-related targets. miRNA-126 is highly expressed in a subset of human B-ALL, and antagonizing miRNA-126 in ALL xenograft models triggered apoptosis and reduced disease burden

Passwords usage and human memory limitations: a survey across age and educational background.

The present article reports a survey conducted to identify the practices on passwords usage, focusing particularly on memory limitations and the use of passwords across individuals with different age and education backgrounds. A total of 263 participants were interviewed, with ages ranging from 18 to 93 years, and education level ranging from grade school to graduate degree. Contrary to our expectations, effects of cognitive decline due to aging were not observed on memory performance for passwords. The results suggested instead, that the number of password uses was the most influential factor on memory performance. That is, as the number of circumstances in which individuals utilized passwords increased, the incidence of forgotten and mixed-up passwords also increased. The theoretical significance of these findings and their implications for good practices on password usage are discussed

Percentage of participants reporting memory difficulties according to age and education.

<p><i>Note:</i> < High = without high school degree, High = high school degree only, College = at least some college education.</p

A control-based observer approach for estimating energy intake during pregnancy

Gestational weight gain outside of Institute of Medicine guidelines poses a risk to both the mother and her unborn child. Behavioral interventions such as Healthy Mom Zone (HMZ) that aim to regulate gestational weight gain require self-monitoring of energy intake, which is often significantly under-reported by participants. This article describes the use of a control systems approach for energy intake estimation during pregnancy. It relies on an energy balance model that predicts gestational weight based on physical activity and energy intake, the latter treated as an unmeasured disturbance. Two control-based observer formulations relying on Internal Model Control and Model Predictive Control, respectively, are presented in this article, first for a hypothetical participant, then on data collected from four HMZ participants. Results demonstrate the effectiveness of the method, with generally best results obtained when estimating energy intake over a weekly time period

Percentage of participants using each type of password according to age and education.

<p><i>Note:</i> < High = without high school degree, High = high school degree only, College = at least some college education, 18–39 y = 18 to 39 years old, 40–64 y = 40 to 64 years old, 65–93 y = 65 to 93 years old.</p

Number of participants by age and education.

<p><i>Note:</i> < High = without high school degree, High = high school degree only, College = at least some college education.</p

Percentage of participants reporting memory difficulties according to number of password uses and education.

<p><i>Note:</i> The percentages on each table bin are based on the data of at least 8 participants. < High = without high school degree, High = high school degree only, College = at least some college education, Pass. uses = number of password uses.</p