Effective mentoring is a key component of academic and career success that contributes to overall measures of productivity. Mentoring relationships also play an important role in mental health and in recruiting and retaining students from groups underrepresented in STEM fields. Despite these clear and measurable benefits, faculty generally do not receive mentorship training, and feedback mechanisms and assessment to improve mentoring in academia are limited. Ineffective mentoring can negatively impact students, faculty, departments, and institutions via decreased productivity, increased stress, and the loss of valuable research products and talented personnel. Thus, there are clear incentives to invest in and implement formal training to improve mentorship in STEM fields. Here, we outline the unique challenges of mentoring in academia and present results from a survey of STEM scientists that support both the need and desire for more formal mentorship training. Using survey results and the primary literature, we identify common behaviors of effective mentors and outline a set of mentorship best practices. We argue that these best practices, as well as the key qualities of flexibility, communication, and trust, are skills that can be taught to prospective and current faculty. We present a model and resources for mentorship training based on our research, which we successfully implemented at the University of Colorado, Boulder, with graduate students and postdocs. We conclude that such training is an important and cost‐effective step toward improving mentorship in STEM fields.
Sexual selection plays a key role in the diversification of numerous animal clades and may accelerate trait divergence during speciation. However, much of our understanding of this process comes from phylogenetic comparative studies, which rely on surrogate measures such as dimorphism that may not represent selection in wild populations. In this study, we assess sexual selection pressures for multiple male visual signals across four barn swallow (Hirundo rustica) populations. Our sample encompassed 2400 linear km and two described subspecies: European H. r. rustica (in the Czech Republic and Romania) and eastern Mediterranean H. r. transitiva (in Israel), as well as a potential area of contact (in Turkey). We demonstrate significant phenotypic differentiation in four sexual signalling axes, despite very low-level genomic divergence and no comparable divergence in an ecological trait. Moreover, the direction of phenotypic divergence is consistent with differences in sexual selection pressures among subspecies. Thus, H. r. transitiva, which have the darkest ventral plumage of any population, experience directional selection for darker plumage. Similarly, H. r. rustica, which have the longest tail feathers of any population, experience directional selection for elongated tail feathers and disruptive selection for ventral plumage saturation. These results suggest that sexual selection is the primary driver of phenotypic differentiation in this species. Our findings add to growing evidence of phenotypic divergence with gene flow. However, to our knowledge, this is the first study to relate direct measures of the strength and targets of sexual selection to phenotypic divergence among closely related wild populations.
Alzheimer's disease (AD) and Parkinson's disease (PD) are the most common neurodegenerative diseases with age as the greatest risk factor. As the general population experiences extended life span, preparation for the prevention and treatment of these and other age-associated neurological diseases are warranted. Since epidemiological studies suggested that non-steroidal anti-inflammatory drug (NSAID) use decreased risk for AD and PD, increasing attention has been devoted to understanding the costs and benefits of the innate neuroinflammatory response to functional recovery following pathology onset. This review will provide a general overview on the role of neuroinflammation in these neurodegenerative diseases and an update on NSAID treatment in recent experimental animal models, epidemiological analyses, and clinical trials.
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