Publications by authors named "M Feldman"

Affordable and clean energy, eliminating poverty, and reducing inequality are important goals of the United Nations Sustainable Development Goals (SDGs). This paper examines the role of access to clean cooking fuels in promoting income growth and reducing income inequality. Using data from Chinese households, we show that a 10% increase in the adoption of clean cooking fuels would result in an increase in total annual household income of US$37 billion nationwide.

View Article and Find Full Text PDF

With its linguistic and cultural diversity, Austronesia is important in the study of evolutionary forces that generate and maintain cultural variation. By analysing publicly available datasets, we have identified four classes of cultural features in Austronesia and distinct clusters within each class. We hypothesized that there are differing modes of transmission and patterns of variation in these cultural classes and that geography alone would be insufficient to explain some of these patterns of variation.

View Article and Find Full Text PDF

Objective: Vagus nerve stimulation (VNS) paired with rehabilitation therapy improved motor status compared to rehabilitation alone in the phase III VNS-REHAB stroke trial, but treatment response was variable and not associated with any clinical measures acquired at baseline, such as age or side of paresis. We hypothesized that neuroimaging measures would be associated with treatment-related gains, examining performance of regional injury measures versus global brain health measures in parallel with clinical measures.

Methods: Baseline magnetic resonance imaging (MRI) scans in the VNS-REHAB trial were used to derive regional injury measures (extent of injury to corticospinal tract, the primary regional measure; plus extent of injury to precentral gyrus and postcentral gyrus; lesion volume; and lesion topography) and global brain health measures (degree of white matter hyperintensities, the primary global brain measure; plus volumes of cerebrospinal fluid, cortical gray matter, white matter, each thalamus, and total brain).

View Article and Find Full Text PDF
Article Synopsis
  • Machine learning, particularly deep learning with convolutional neural networks (CNNs), is being used to detect prostate cancer in tissue slides, but sample type differences affect model accuracy.
  • Research tested whether CNNs trained on one type of sample (biopsy or radical prostatectomy) could effectively analyze the other type, revealing a significant drop in performance across sample types.
  • Results indicated that models performed well on their own sample but poorly on the alternative type, highlighting the need to consider morphological differences in training to improve cancer detection accuracy in clinical settings.*
View Article and Find Full Text PDF