Flexible Systems in HR

Editorial special issueThe final publication is available at Springer via http://dx.doi.org/10.1007/s40171-016-0125-5Research on the concept of ‘flexibility’ has produced a substantive scholarly in recent decades and has evolved as a focal area of management research (Mønsted 1991; Sushil 1994; Skipper et al. 2014; Krishna et al. 2015). According to Sushil (2001) ‘Flexibility offers freedom of choice and is highly context specific’. Here, ‘context specific’ refers to the role of contingencies within flexibility which might render it as a form of a firm’s dynamic capability. The dynamic capability scholarship argues that in order to achieve excellence, organizations should develop capabilities complementary to their competencies (Teece et al. 1997; Helfat and Peteraf 2009). Thus, flexible HR practices can help organizations in achieving sustainable competitiveness through creating, integrating, reconfiguring, and building on its human resource base. For example, organizations can achieve competitive edge by customizing training and development programs

Socially and biologically inspired computing for self-organizing communications networks

The design and development of future communications networks call for a careful examination of biological and social systems. New technological developments like self-driving cars, wireless sensor networks, drones swarm, Internet of Things, Big Data, and Blockchain are promoting an integration process that will bring together all those technologies in a large-scale heterogeneous network. Most of the challenges related to these new developments cannot be faced using traditional approaches, and require to explore novel paradigms for building computational mechanisms that allow us to deal with the emergent complexity of these new applications. In this article, we show that it is possible to use biologically and socially inspired computing for designing and implementing self-organizing communication systems. We argue that an abstract analysis of biological and social phenomena can be made to develop computational models that provide a suitable conceptual framework for building new networking technologies: biologically inspired computing for achieving efficient and scalable networking under uncertain environments; socially inspired computing for increasing the capacity of a system for solving problems through collective actions. We aim to enhance the state-of-the-art of these approaches and encourage other researchers to use these models in their future work

Telocytes and putative stem cells in the lungs: electron microscopy, electron tomography and laser scanning microscopy

This study describes a novel type of interstitial (stromal) cell — telocytes (TCs) — in the human and mouse respiratory tree (terminal and respiratory bronchioles, as well as alveolar ducts). TCs have recently been described in pleura, epicardium, myocardium, endocardium, intestine, uterus, pancreas, mammary gland, etc. (see www.telocytes.com). TCs are cells with specific prolongations called telopodes (Tp), frequently two to three per cell. Tp are very long prolongations (tens up to hundreds of μm) built of alternating thin segments known as podomers (≤ 200 nm, below the resolving power of light microscope) and dilated segments called podoms, which accommodate mitochondria, rough endoplasmic reticulum and caveolae. Tp ramify dichotomously, making a 3-dimensional network with complex homo- and heterocellular junctions. Confocal microscopy reveals that TCs are c-kit- and CD34-positive. Tp release shed vesicles or exosomes, sending macromolecular signals to neighboring cells and eventually modifying their transcriptional activity. At bronchoalveolar junctions, TCs have been observed in close association with putative stem cells (SCs) in the subepithelial stroma. SCs are recognized by their ultrastructure and Sca-1 positivity. Tp surround SCs, forming complex TC-SC niches (TC-SCNs). Electron tomography allows the identification of bridging nanostructures, which connect Tp with SCs. In conclusion, this study shows the presence of TCs in lungs and identifies a TC-SC tandem in subepithelial niches of the bronchiolar tree. In TC-SCNs, the synergy of TCs and SCs may be based on nanocontacts and shed vesicles

Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data.

Trio-based whole-exome sequence (WES) data have established confident genetic diagnoses in ∼40% of previously undiagnosed individuals recruited to the Deciphering Developmental Disorders (DDD) study. Here we aim to use the breadth of phenotypic information recorded in DDD to augment diagnosis and disease variant discovery in probands. Median Euclidean distances (mEuD) were employed as a simple measure of similarity of quantitative phenotypic data within sets of ≥10 individuals with plausibly causative de novo mutations (DNM) in 28 different developmental disorder genes. 13/28 (46.4%) showed significant similarity for growth or developmental milestone metrics, 10/28 (35.7%) showed similarity in HPO term usage, and 12/28 (43%) showed no phenotypic similarity. Pairwise comparisons of individuals with high-impact inherited variants to the 32 individuals with causative DNM in ANKRD11 using only growth z-scores highlighted 5 likely causative inherited variants and two unrecognized DNM resulting in an 18% diagnostic uplift for this gene. Using an independent approach, naive Bayes classification of growth and developmental data produced reasonably discriminative models for the 24 DNM genes with sufficiently complete data. An unsupervised naive Bayes classification of 6,993 probands with WES data and sufficient phenotypic information defined 23 in silico syndromes (ISSs) and was used to test a "phenotype first" approach to the discovery of causative genotypes using WES variants strictly filtered on allele frequency, mutation consequence, and evidence of constraint in humans. This highlighted heterozygous de novo nonsynonymous variants in SPTBN2 as causative in three DDD probands