Unpacking Indigenous Social Mobility: Entrepreneurs, Social Networks, and Connections to Culture.

In settler societies, upward social mobility by Indigenous people is seen in the growth of successful professional and entrepreneurial classes where both wealth creation and social power are significant resources. Yet, public and academic discourses perpetuate the belief that social mobility impacts negatively on Indigenous people by placing cultural identity in conflict with capitalist business practices. Using data from an international comparison consisting of interviews with 220 Indigenous entrepreneurs in research sites across three countries, this article shows that the belief is unfounded and reveals how this duality creates an impossible tension when Indigenous cultural identity is framed as "at risk" because of social mobility.

Data-driven development and validation of a nutrient-based score to measure nutritional balance of meals in the Philippines.

Background: The goal of the present study was to design an easily computable score, based on nutrient composition data instead of food groups, to evaluate the nutritional quality and balance of meals, adapted to the nutritional recommendations for Filipino adults.

Method: The score was defined as a weighted average of 9 nutrient scores. Protein, total fat, saturated fat, free sugars were scored as % of energy; calcium, fiber, sodium, vitamin C, magnesium were scored based on the local dietary reference intakes.

Effects of 3D ultrasonography and 3D printed images on maternal-fetal attachment and its correlation with overall smoking within pregnancy: a pilot study.

Background: Smoking in pregnancy continues to cause significant morbidity to mothers and babies and contributes to tremendous costs to society. Maternal-fetal attachment (MFA) may differentiate smokers who quit or pregnant smokers from non-smokers. Researchers have recommended utilizing interventions that improve MFA to help decrease smoking within pregnancy.

Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis.

Deep learning-assisted digital pathology has demonstrated the potential to profoundly impact clinical practice, even surpassing human pathologists in performance. However, as deep neural network (DNN) architectures grow in size and complexity, their explainability decreases, posing challenges in interpreting pathology features for broader clinical insights into physiological diseases. To better assess the interpretability of digital microscopic images and guide future microscopic system design, we developed a novel method to study the predictive feature length-scale that underpins a DNN's predictive power.