Although there is a growing interest toward the topic of leader humility, extant research has largely failed to consider the underlying mechanisms through which leader humility influences team outcomes. In this research, we integrate the emerging literature of leader humility and social information processing theory to theorize how leader humility facilitates the development of collective team psychological capital, leading to higher team task allocation effectiveness and team performance. While Owens and Hekman (2016) suggest that leader humility has homogeneous effects on followers, we propose a potential heterogeneous effect based on the complementarity literature (e.g., Tiedens, Unzueta, & Young, 2007) and the principle of equifinality (leaders may influence team outcomes through multiple pathways; Morgeson, DeRue, & Karam, 2010). In three studies conducted in China, Singapore, and Portugal, including an experiment, a multisource field study, and a three-wave multisource field study, we find support for our hypotheses that leader humility enhances team performance serially through increased team psychological capital and team task allocation effectiveness. We discuss the theoretical implications of our work to the leader humility, psychological capital, and team effectiveness literatures; and offer suggestions for future research.

a b s t r a c tIn this paper we introduce the notion of distance k-guarding applied to triangulation graphs, and associate it with distance k-domination and distance k-covering. We obtain results for maximal outerplanar graphs when k = 2. A set S of vertices in a triangulation graph T is a distance 2-guarding set (or 2d-guarding set for short) if every face of T has a vertex adjacent to a vertex of S. We show that ⌊ n 5 ⌋ (respectively, ⌊ n 4 ⌋) vertices are sufficient to 2d-guard and 2d-dominate (respectively, 2d-cover) any n-vertex maximal outerplanar graph. We also show that these bounds are tight.

BackgroundProtein cavities play a key role in biomolecular recognition and function, particularly in protein-ligand interactions, as usual in drug discovery and design. Grid-based cavity detection methods aim at finding cavities as aggregates of grid nodes outside the molecule, under the condition that such cavities are bracketed by nodes on the molecule surface along a set of directions (not necessarily aligned with coordinate axes). Therefore, these methods are sensitive to scanning directions, a problem that we call cavity ground-and-walls ambiguity, i.e., they depend on the position and orientation of the protein in the discretized domain. Also, it is hard to distinguish grid nodes belonging to protein cavities amongst all those outside the protein, a problem that we call cavity ceiling ambiguity.ResultsWe solve those two ambiguity problems using two implicit isosurfaces of the protein, the protein surface itself (called inner isosurface) that excludes all its interior nodes from any cavity, and the outer isosurface that excludes most of its exterior nodes from any cavity. Summing up, the cavities are formed from nodes located between these two isosurfaces. It is worth noting that these two surfaces do not need to be evaluated (i.e., sampled), triangulated, and rendered on the screen to find the cavities in between; their defining analytic functions are enough to determine which grid nodes are in the empty space between them.ConclusionThis article introduces a novel geometric algorithm to detect cavities on the protein surface that takes advantage of the real analytic functions describing two Gaussian surfaces of a given protein.

A b stractIn this article we study some variants of the domination concept attending to the connectivity of the subgraph generated by the dominant set. This study is restricted to maximal outerplanar graphs. We establish tight combinatorial bounds for con nected domination, semitotal domination, independent domination and weakly con nected domination for any n-vertex maximal outerplaner graph.

Extensive research has been applied to discover new techniques and methods to model protein-ligand interactions. In particular, considerable efforts focused on identifying candidate binding sites, which quite often are active sites that correspond to protein pockets or cavities. Thus, these cavities play an important role in molecular docking. However, there is no established benchmark to assess the accuracy of new cavity detection methods. In practice, each new technique is evaluated using a small set of proteins with known binding sites as ground-truth. However, studies supported by large datasets of known cavities and/or binding sites and statistical classification (i.e., false positives, false negatives, true positives, and true negatives) would yield much stronger and reliable assessments. To this end, we propose CavBench, a generic and extensible benchmark to compare different cavity detection methods relative to diverse ground truth datasets (e.g., PDBsum) using statistical classification methods.