Abacavir induced T cell reactivity from drug naïve individuals shares features of allo-immune responses.

Abacavir hypersensitivity is a severe hypersensitivity reaction which occurs exclusively in carriers of the HLA-B*57∶01 allele. In vitro culture of PBMC with abacavir results in the outgrowth of abacavir-reacting CD8+ T cells, which release IFNγ and are cytotoxic. How this immune response is induced and what is recognized by these T cells is still a matter of debate. We analyzed the conditions required to develop an abacavir-dependent T cell response in vitro. The abacavir reactivity was independent of co-stimulatory signals, as neither DC maturation nor release of inflammatory cytokines were observed upon abacavir exposure. Abacavir induced T cells arose in the absence of professional APC and stemmed from naïve and memory compartments. These features are reminiscent of allo-reactivity. Screening for allo-reactivity revealed that about 5% of generated T cell clones (n = 136) from three donors were allo-reactive exclusively to the related HLA-B*58∶01. The addition of peptides which can bind to the HLA-B*57∶01-abacavir complex and to HLA-B*58∶01 during the induction phase increased the proportion of HLA-B*58∶01 allo-reactive T cell clones from 5% to 42%. In conclusion, abacavir can alter the HLA-B*57∶01-peptide complex in a way that mimics an allo-allele ('altered self-allele') and create the potential for robust T cell responses.

PSHARE: Stata module to compute and graph percentile shares

-pshare- computes and graphs percentile shares from individual level data. Percentile shares are often used in inequality research to study the distribution of income or wealth. They are defined as differences between Lorenz ordinates of the outcome variable. Technically, the observations are sorted in increasing order of the outcome variable and the specified percentiles are computed from the running sum of the outcomes. Percentile shares are then computed as differences between percentiles, divided by total outcome. pshare requires moremata to be installed on the system; see ssc describe moremata.

A new Stata command for computing and graphing percentile shares

Percentile shares provide an intuitive and easy-to-understand way for analyzing income or wealth distributions. A celebrated example are the top income shares sported by the works of Thomas Piketty and colleagues. Moreover, series of percentile shares, defined as differences between Lorenz ordinates, can be used to visualize whole distributions or changes in distributions. In this talk, I present a new command called pshare that computes and graphs percentile shares (or changes in percentile shares) from individual level data. The command also provides confidence intervals and supports survey estimation.

A new Stata command for computing and graphing percentile shares

Percentile shares provide an intuitive and easy-to-understand way for analyzing income or wealth distributions. A celebrated example is the top income shares sported by the works of Thomas Piketty and colleagues. Moreover, series of percentile shares, defined as differences between Lorenz ordinates, can be used to visualize whole distributions or changes in distributions. In this talk, I present a new command called pshare that computes and graphs percentile shares (or changes in percentile shares) from individual level data. The command also provides confidence intervals and supports survey estimation.

Assessing inequality using percentile shares

At least since Thomas Piketty's best-selling \Capital in the Twenty- First Century" (2014, Cambridge, MA: The Belknap Press), percentile shares have become a popular approach for analyzing distributional inequalities. In their work on the development of top incomes, Piketty and collaborators typically report top- percentage shares, using varying percentages as thresholds (top 10%, top 1%, top 0.1%, etc.). However, analysis of percentile shares at other positions in the distri- bution may also be of interest. In this paper I present a new Stata command called pshare that estimates percentile shares from individual-level data and displays the results using histograms or stacked bar charts.

Assessing inequality using percentile shares

At least since Thomas Piketty's best-selling "Capital in the Twenty-First Century" (2014, Cambridge, MA: The Belknap Press), percentile shares have become a popular approach for analyzing distributional inequalities. In their work on the development of top incomes, Piketty and collaborators typically report top-percentage shares, using varying percentages as thresholds (top 10%, top 1%, top 0.1%, etc.). However, analysis of percentile shares at other positions in the distribution may also be of interest. In this paper I present a new Stata command called -pshare- that estimates percentile shares from individual-level data and displays the results using histograms or stacked bar charts.

Portfolio Entrepreneurship in Family Firms: A Resource-based Perspective

The phenomenon of portfolio entrepreneurship has attracted considerable scholarly attention and is particularly relevant in the family fi rm context. However, there is a lack of knowledge of the process through which portfolio entrepreneurship develops in family firms. We address this gap by analyzing four in-depth, longitudinal family firm case studies from Europe and Latin America. Using a resource-based perspective, we identify six distinct resource categories that are relevant to the portfolio entrepreneurship process. Furthermore, we reveal that their importance varies across time. Our resulting resource-based process model of portfolio entrepreneurship in family firms makes valuable contributions to both theory and practice.