Mentorship is empirically related to several desired outcomes in college students including academic success and career development. Yet little is known about how mentorship aids leadership development in college students. This study uses data from the Multi-Institutional Study of Leadership, a national study with more than 110,000 participants from 101 institutions, to explore this issue. Findings show that leadership capacity is influenced by the mentorship process and the type of mentor (faculty, staff, employer, or peer). By focusing on who does the mentoring and how the mentoring process unfolds, this study informs best practices in mentoring for student leadership development.

Aims: Evaluating expression of the human epidermal growth factor receptor 2 (HER2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognized importance as a predictive and prognostic marker in clinical practice. However, visual scoring of HER2 is subjective, and consequently prone to interobserver variability. Given the prognostic and therapeutic implications of HER2 scoring, a more objective method is required. In this paper, we report on a recent automated HER2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art artificial intelligence (AI)-based automated methods for HER2 scoring. Methods and results: The contest data set comprised digitized whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both haematoxylin and eosin (H&E) and IHC for HER2. The contesting algorithms predicted scores of the IHC slides automatically for an unseen subset of the data set and the predicted scores were compared with the 'ground truth' (a consensus score from at least two experts). We also report on a simple 'Man versus Machine' contest for the scoring of HER2 and show Address for correspondence: N Rajpoot and T Qaiser, Department of Computer Science, University of Warwick, UK. e-mails: n.m.rajpoot@ warwick.ac.uk; t.qaiser@warwick.ac.uk *These authors contributed equally to this study. 2018 , 72, 227-238. DOI: 10.1111 that the automated methods could beat the pathology experts on this contest data set. Conclusions: This paper presents a benchmark for comparing the performance of automated algorithms for scoring of HER2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring.

The enzyme α-methylacyl CoA racemase (AMACR) is involved in the metabolism of branched-chain fatty acids and has been identified as a promising therapeutic target for prostate cancer. By using the recently available human AMACR from HEK293 kidney cell cultures, we tested a series of new rationally designed inhibitors to determine the structural requirements in the acyl component. An N-methylthiocarbamate (Ki=98 nM), designed to mimic the proposed enzyme-bound enolate, was found to be the most potent AMACR inhibitor reported to date.

Independent interpretation of a variety of pulse sequences may maximise detection of cartilage and bone lesions in the fetlock. Clinicians should be aware of potential false positive and negative results.