Feeling unsure: Quit or stay? Uncovering heterogeneity in employees\u27 intention to leave in Indian call centers

Employee turnover remains to be one of the biggest human resource problems facing the Indian international call center industry. This paper aims to provide a comprehensive study of how the attitudes of call center employees toward different aspects of their work affect their intention to leave. Our specific contribution to the literature is in understanding the heterogeneity among employees and how this affects meaningful inference in studying employees\u27 intention to leave. To achieve this goal, we compare and contrast between traditional ordinary least squares regression models that have been used in the extant literature with latent class analysis. Latent class analysis suggests the presence of three distinct groups of employees, thus confirming the heterogeneity present in the data. The three groups can be represented as the two polar groups, one keen on staying and the other keen on leaving, and a significantly large third group of employees who are unsure. We also find that the impact of different attitudes vary between groups in terms of both economic significance (magnitude of coefficients), and statistical significance. This study throws important light on the research on turnover and has significant research and practical implications. © 2013 Copyright Taylor and Francis Group, LLC

Finding a Good Job: Academic Network Centrality and Early Occupational Outcomes in Management Academia

The impact of universalistic versus particularistic criteria on academic hiring has been receiving growing attention in recent years. Yet, most studies conducted on hiring norms in academy and management academy have ignored the impact of social capital, particularly structural social capital, a particularistic attribute, on occupational outcomes. This could lead to a partial if not misleading view of the sociology of hiring in management academy. We utilize a novel approach, focusing on academic departments’ structural social capital in the form of network centrality (based on cumulative PhD exchange networks), and explore how this type of centrality impacts job seekers’ occupational prestige for new academic jobs in management departments and early career quality publications.We find that although merit-based criteria such as publications matter somewhat, academic network centrality explains significant variance in obtaining prestigious jobs. Paradoxically, we find that academic network centrality does not explain early career publications. We discuss the implications of our findings for management science

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.

BackgroundSingle-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.ResultsWe introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods.ConclusionsSlingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression

clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets

Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells. It is common in the detection of novel subtypes to run many clustering algorithms, as well as rely on subsampling and ensemble methods to improve robustness. We introduce a Bioconductor R package, clusterExperiment, that implements a general and flexible strategy we entitle Resampling-based Sequential Ensemble Clustering (RSEC). RSEC enables the user to easily create multiple, competing clusterings of the data based on different techniques and associated tuning parameters, including easy integration of resampling and sequential clustering, and then provides methods for consolidating the multiple clusterings into a final consensus clustering. The package is modular and allows the user to separately apply the individual components of the RSEC procedure, i.e., apply multiple clustering algorithms, create a consensus clustering or choose tuning parameters, and merge clusters. Additionally, clusterExperiment provides a variety of visualization tools for the clustering process, as well as methods for the identification of possible cluster signatures or biomarkers. The R package clusterExperiment is publicly available through the Bioconductor Project, with a detailed manual (vignette) as well as well documented help pages for each function.</div

Distribution models of deep-sea elasmobranchs in the Azores, Mid-Atlantic Ridge, to inform spatial planning

Elasmobranchs inhabiting depths beyond 200 m are extremely susceptible to overexploitation but are extracted by fisheries around the world either as target species or as bycatch. There is little information available to formulate management strategies to reduce elasmobranch-fishery interactions in the deep sea. In European Union waters, prohibiting the catches of deep-sea elasmobranchs has provided the necessary impetus to study by-catch avoidance of these threatened species. We used over 20 years of fisheries-independent and fisheries-dependent data to model the spatial distribution of 15 species of deep-sea elasmobranchs (12 sharks and 3 rays) captured frequently in the Exclusive Economic Zone of the Azores Archipelago (Mid-Atlantic Ridge) to explore spatial management to reduce unwanted catches of these species. We applied Generalised Additive Models to predict the probability of presence of 15 species, as well as the abundance of 6 of those species, within the Azores EEZ and neighbouring seamounts (up to 2000 m depth), using environmental and operational variables as predictors. Our results identified that depth is most influential in determining the distribution of these sharks and rays, in addition to seafloor topography. Distinctive bathymetric features such as seamounts and ridges were highlighted as areas where the probability of presence of the greatest number of species overlapped. Although not related to habitat, gear type influenced the capture probability of certain species, with the artisanal handline, gorazeira, having lower captures than bottom longline. Our results support using depth-based, area-based, and gear-based tactics to design management measures to reduce elasmobranch bycatch, for more sustainable deep-sea fisheries.Postprint2,42

Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia

Abstract:Altered olfactory function is a common symptom of COVID-19, but its etiology is unknown. A key question is whether SARS-CoV-2 (CoV-2) – the causal agent in COVID-19 – affects olfaction directly, by infecting olfactory sensory neurons or their targets in the olfactory bulb, or indirectly, through perturbation of supporting cells. Here we identify cell types in the olfactory epithelium and olfactory bulb that express SARS-CoV-2 cell entry molecules. Bulk sequencing demonstrated that mouse, non-human primate and human olfactory mucosa expresses two key genes involved in CoV-2 entry, ACE2 and TMPRSS2. However, single cell sequencing revealed that ACE2 is expressed in support cells, stem cells, and perivascular cells, rather than in neurons. Immunostaining confirmed these results and revealed pervasive expression of ACE2 protein in dorsally-located olfactory epithelial sustentacular cells and olfactory bulb pericytes in the mouse. These findings suggest that CoV-2 infection of non-neuronal cell types leads to anosmia and related disturbances in odor perception in COVID-19 patients