No abstract

Great interest remains in finding new and emerging therapies for the treatment of male and female pattern hair loss. The autologous fat grafting technique is >100 years old, with a recent and dramatic increase in clinical experience over the past 10–15 years. Recently, in 2001, Zuk et al published the presence of adipose-derived stem cells, and abundant research has shown that adipose is a complex, biological active, and important tissue. Festa et al, in 2011, reported that adipocyte lineage cells support the stem cell niche and help drive the complex hair growth cycle. Adipose-derived regenerative cells (also known as stromal vascular fraction [SVF]) is a heterogeneous group of noncultured cells that can be reliably extracted from adipose by using automated systems, and these cells work largely by paracrine mechanisms to support adipocyte viability. While, today, autologous fat is transplanted primarily for esthetic and reconstructive volume, surgeons have previously reported positive skin and hair changes posttransplantation. This follicular regenerative approach is intriguing and raises the possibility that one can drive or restore the hair cycle in male and female pattern baldness by stimulating the niche with autologous fat enriched with SVF. In this first of a kind patient series, the authors report on the safety, tolerability, and quantitative, as well as photographic changes, in a group of patients with early genetic alopecia treated with subcutaneous scalp injection of enriched adipose tissue. The findings suggest that scalp stem cell-enriched fat grafting may represent a promising alternative approach to treating baldness in men and women.

No abstract

Entity matching (EM) has been a long-standing challenge in data management. Most current EM works, however, focus only on developing matching algorithms. We argue that far more efforts should be devoted to building EM systems. We discuss the limitations of current EM systems, then present Magellan, a new kind of EM systems that addresses these limitations. Magellan is novel in four important aspects. (1) It provides a how-to guide that tells users what to do in each EM scenario, step by step. (2) It provides tools to help users do these steps; the tools seek to cover the entire EM pipeline, not just matching and blocking as current EM systems do. (3) Tools are built on top of the data science stacks in Python, allowing Magellan to borrow a rich set of capabilities in data cleaning, IE, visualization, learning, etc. (4) Magellan provide a powerful scripting environment to facilitate interactive experimentation and allow users to quickly write code to "patch" the system. We have extensively evaluated Magellan with 44 students and users at various organizations. In this paper we propose demonstration scenarios that show the promise of the Magellan approach.

Entity matching (EM) has been a long-standing challenge in data management. Most current EM works focus only on developing matching algorithms. We argue that far more efforts should be devoted to building EM systems. We discuss the limitations of current EM systems, then present as a solution Magellan, a new kind of EM systems. Magellan is novel in four important aspects. (1) It provides how-to guides that tell users what to do in each EM scenario, step by step. (2) It provides tools to help users do these steps; the tools seek to cover the entire EM pipeline, not just matching and blocking as current EM systems do. (3) Tools are built on top of the data analysis and Big Data stacks in Python, allowing Magellan to borrow a rich set of capabilities in data cleaning, IE, visualization, learning, etc. (4) Magellan provides a powerful scripting environment to facilitate interactive experimentation and quick "patching" of the system. We describe research challenges raised by Magellan, then present extensive experiments with 44 students and users at several organizations that show the promise of the Magellan approach.