Pharmacological rescue of adult hippocampal neurogenesis in a mouse model of X-linked intellectual disability

Oligophrenin-1 (OPHN1) is a Rho GTPase activating protein whose mutations cause X-linked intellectual disability (XLID). How loss of function of Ophnl affects neuronal development is only partly understood. Here we have exploited adult hippocampal neurogenesis to dissect the steps of neuronal differentiation that are affected by Ophn1 deletion. We found that mice lacking Ophnl display a reduction in the number of newborn neurons in the dentate gyrus. A significant fraction of the Ophn1-deficient newly generated neurons failed to extend an axon towards CM, and showed an altered density of dendritic protrusions. Since Ophnl-deficient mice display overactivation of Rho-associated protein kinase (ROCK) and protein kinase A (PICA) signaling, we administered a clinically approved ROCK/PICA inhibitor (fasudil) to correct the neurogenesis defects. While administration of fasudil was not effective in rescuing axon formation, the same treatment completely restored spine density to control levels, and enhanced the long-term survival of adult-born neurons in mice lacking Ophn1. These results identify specific neurodevelopmental steps that are impacted by Ophn1 deletion, and indicate that they may be at least partially corrected by pharmacological treatment. (C) 2017 The Authors. Published by Elsevier Inc

Implementing reshoring: insights and principles from a longitudinal case study in the e-bike industry

Despite the growing body of literature on firms revising their production offshoring decisions, there is scarce research on how reshoring is actually implemented. This paper responds to this gap by analysing the case of FIVE, an Italian electric bike (e-bike) company that has insourced and relocated its production activities - originally outsourced to a Chinese manufacturer - to its home country. The research combines a design science approach with a longitudinal single case study method to gather both theoretical insights and practical managerial advice on how to conduct the reshoring implementation. The study captures the dynamic nature of the implementation process, showing how its elements evolve over time. Organisational learning emerges as a driving factor of reshoring. Each of the implementation stages is characterised by the development of a specific organisational process, which provides the know-how required for performing tasks at that particular stage. From a practical perspective, the study develops five reshoring implementation principles and a three-stage implementation process, thereby offering valuable guidelines for managers of SMEs who wish to undertake the reshoring decision

Workplace Bullying and Post-Traumatic Stress Disorder Symptomology: The Influence of Role Conflict and the Moderating Effects of Neuroticism and Managerial Competencies

Research has explored numerous consequences of workplace bullying, including a recent link to the exhibition of post-traumatic stress disorder (PTSD) symptomology. Role conflict as a workplace stressor may contribute to instances of bullying from a passive perspective, which may lead to PTSD symptomology in victims. What remains less explored is if role conflict has a direct relationship to PTSD symptomology and how personality traits such as neuroticism and workplace factors such as managerial competencies may moderate the stress brought on by role conflict. Hence the present study seeks to examine this gap in the literature. This study utilizes a between-subjects, cross-sectional design with 159 participants, 39.6% male and 60.4% female. Most participants (60%) were Italian workers of a large social cooperative organization. Confirmatory factor analysis indicated that the measurement model was valid and had an adequate model fit. Results from two separate moderated mediation analyses found a positive, full mediation between the independent variable of role conflict, the mediator of exposure to bullying, and the dependent variable of PTSD symptomology. Furthermore, in this study, neuroticism strengthened the indirect effect while managerial competencies weakened it. The results highlight the importance of training competent managers and providing resources for more vulnerable employees to moderate employee work stress and its negative outcomes

A weekly diary within-individual investigation of the relationship between exposure to bullying behavior, workplace phobia, and posttraumatic stress symptomatology

Most studies on workplace bullying have adopted a between-person approach, neglecting the potential within-individual fluctuations in the experience of bullying behaviors. However, investigating such fluctuations may prove useful for uncovering processes and mechanisms associated with bullying and its antecedents and consequences as they unfold over time. In the present study, based on recent discoveries on traumatic experiences and posttraumatic stress (PTS), we hypothesized that even short-term exposure to bullying behaviors—such as the exposure that characterizes an individual when the time window considered is a working week—may already have a substantial psychological impact at the within-individual level, as indicated by the experience of PTS symptoms. Additionally, we hypothesized that the development of workplace phobia may act as a mechanism linking the exposure to bullying behaviors during the week and the reported PTS symptomatology, and that person-level vulnerability factors to PTS (e.g., a recent trauma and female gender) accentuate the within-individual relationships. We tested the proposed hypotheses on a sample of 158 workers that were followed for 6 consecutive working weeks for a total of 860 observations. In line with other recent within-individual investigations, we found that exposure to bullying behaviors shows substantial week-level fluctuations. We also found overall support for the hypotheses, including evidence of a within-level lagged impact of bullying behaviors on workplace phobia, suggesting that even nonpersistent exposure to such behaviors is related to potentially nonignorable psychological suffering and PTS symptoms

Multiple-point statistical simulation for hydrogeological models: 3D training image development and conditioning strategies

Most studies about the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level, structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2D or quasi-3D training images. In the present study, we demonstrate a novel strategy for 3D MPS modelling characterized by: (i) realistic 3D training images, and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed sand/clay spatial trends. The training image is constructed as a small 3D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, the study underlines that it is important to consider both the geological environment, and the type and quality of input information in order to achieve optimal results from MPS modelling. In this study we present a possible workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modellin

Coping with negative stereotypes toward older workers: Organizational and work-related outcomes

The current study aims to test a moderated-mediation model in which occupational selfefficacy determines the indirect effect of negative stereotypes about older workers in the organization both on psychological engagement in the work domain and on attitudes toward development opportunities through identification with the company. The survey involved 1,501 Italian subjects aged over 50 who were employed by a major large-scale retailer. Consistently with the Social Identity Theory and the Social Exchange Theory, results showed that the perception of negative stereotypes about older workers in the organization is associated with low identification with the company and, subsequently, with poor psychological engagement in the work domain and with attitudes indicating very little interest in development opportunities. In addition, this association was found to be stronger in older workers with higher and medium levels of occupational selfefficacy. These findings suggest that organizations should discourage the dissemination of negative stereotypes about older workers in the workplace because they may lead to older workers' disengagement from the work domain and their loss of interest in development opportunities

A Low Complexity Rolling Bearing Diagnosis Technique Based on Machine Learning and Smart Preprocessing

In this work, we present a diagnosis system for rolling bearings that leverages simultaneous measurements of vibrations and machine rotation speed. Our approach combines the robustness of simple time domain methods for fault detection with the potential of machine learning techniques for fault location. This research is based on a neural network classifier, which exploits a simple and novel preprocessing algorithm specifically designed for minimizing the dependency of the classifier performance on the machine working conditions, on the bearing model and on the acquisition system set-up. The overall diagnosis system is based on light algorithms with reduced complexity and hardware resource demand and is designed to be deployed in embedded electronics. The fault diagnosis system was trained using emulated data, exploiting an ad-hoc test bench thus avoiding the problem of generating enough data, achieving an overall classifier accuracy larger than 98%. Its noteworthy ability to generalize was proven by using data emulating different working conditions and acquisition set-ups and noise levels, obtaining in all the cases accuracies greater than 97%, thereby proving in this way that the proposed system can be applied in a wide spectrum of different applications. Finally, real data from an on-line database containing vibration signals obtained in a completely different scenario are used to demonstrate the distinctive capability of the proposed system to generalize