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Despite the dramatically increased entry of women into general surgery and surgical subspecialties, traditionally male-dominated fields, there remains a gross under-representation of women in the leadership positions of these departments. Women begin their careers with fewer academic resources and tend to progress through the ranks slower than men. Female surgeons also receive significantly lower salaries than their male counterparts and are more vulnerable to discrimination, both obvious and covert. Although some argue that female surgeons tend to choose their families over careers, studies have actually shown that women are as eager as men to assume leadership positions, are equally qualified for these positions as men, and are as good as men at leadership tasks.Three major constraints contribute to the glass-ceiling phenomenon: traditional gender roles, manifestations of sexism in the medical environment, and lack of effective mentors. Gender roles contribute to unconscious assumptions that have little to do with actual knowledge and abilities of an individuals and they negatively influence decision-making when it comes to promotions. Sexism has many forms, from subtle to explicit forms, and some studies show that far more women report being discriminately against than do men. There is a lack of same-sex mentors and role models for women in academic surgery, thereby isolating female academicians further. This review summarizes the manifestation of the glass-ceiling phenomenon, identifies some causes of these inequalities, and proposes different strategies for continuing the advancement of women in academic surgery and to shatter the glass ceiling.

The purpose of this paper is to describe a framework for evaluating image segmentation algorithms. Image segmentation consists of object recognition and delineation. For evaluating segmentation methods, three factors-precision (reliability), accuracy (validity), and efficiency (viability)-need to be considered for both recognition and delineation. To assess precision, we need to choose a figure of merit, repeat segmentation considering all sources of variation, and determine variations in figure of merit via statistical analysis. It is impossible usually to establish true segmentation. Hence, to assess accuracy, we need to choose a surrogate of true segmentation and proceed as for precision. In determining accuracy, it may be important to consider different 'landmark' areas of the structure to be segmented depending on the application. To assess efficiency, both the computational and the user time required for algorithm training and for algorithm execution should be measured and analyzed. Precision, accuracy, and efficiency factors have an influence on one another. It is difficult to improve one factor without affecting others. Segmentation methods must be compared based on all three factors, as illustrated in an example wherein two methods are compared in a particular application domain. The weight given to each factor depends on application. q

Mesenchymal stem cells (MSCs) are a heterogeneous population of stem/progenitor cells with pluripotent capacity to differentiate into mesodermal and non-mesodermal cell lineages, including osteocytes, adipocytes, chondrocytes, myocytes, cardiomyocytes, fibroblasts, myofibroblasts, epithelial cells, and neurons. MSCs reside primarily in the bone marrow, but also exist in other sites such as adipose tissue, peripheral blood, cord blood, liver, and fetal tissues. When stimulated by specific signals, these cells can be released from their niche in the bone marrow into circulation and recruited to the target tissues where they undergo in situ differentiation and contribute to tissue regeneration and homeostasis. Several characteristics of MSCs, such as the potential to differentiate into multiple lineages and the ability to be expanded ex vivo while retaining their original lineage differentiation commitment, make these cells very interesting targets for potential therapeutic use in regenerative medicine and tissue engineering. The feasibility for transplantation of primary or engineered MSCs as cellbased therapy has been demonstrated. In this review, we summarize the current knowledge on the signals that control trafficking and differentiation of MSCs.

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The purpose of this paper is to describe a framework for evaluating image segmentation algorithms. Image segmentation consists of object recognition and delineation. For evaluating segmentation methods, three factors -precision (reproducibility), accuracy (agreement with truth), and efficiency (time taken) -need to be considered for both recognition and delineation. To assess precision, we need to choose a figure of merit (FOM), repeat segmentation considering all sources of variation, and determine variations in FOM via statistical analysis. It is impossible usually to establish true segmentation. Hence, to assess accuracy, we need to choose a surrogate of true segmentation and proceed as for precision. To assess efficiency, both the computational and the user time required for algorithm and operator training and for algorithm execution should be measured and analyzed. Precision, accuracy, and efficiency are interdependent. It is difficult to improve one factor without affecting others. Segmentation methods must be compared based on all three factors. The weight given to each factor depends on application.

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