Employees balance and stability as key points in organizational performance

System analyses deal with interrelationships between different variables that keep the system in balance. In many analysis of complex thinking, a system is viewed as a complex unit in which the ‘whole’ is not reduced to the ‘sum’ of its parts; the system becomes an ambiguous item because it consists of several entities that interact with unforeseen results or, in other words, it is situated at a transdisciplinary level, it is impossible for an area to have a complete reading of its complexity. It was also mentioned that the concept of the open system best describes complexity by stating that ‘the laws of the organization are not equilibrium, but an imbalance that is restored or compensated for by stabilized dynamics’. This idea originated from the field of thermodynamics and the second law, in which the imbalance that it maintains allows the system for an apparent balance. This fragile steady state has something of a paradox, since the structures remain the same, but their constituents are changeable. The concept of open system undoes the door to a theory of evolution that can only derive from the interactions between a system and its ecosystem. Within this systemic approach, the focus of the analysis takes into account the ambiguity, multidisciplinary and complexity associated with system adjustment, i.e. it is intended to qualify an employee job based on their experience and knowledge as a measure of their impact on the organization performance

Histological validation of high-resolution DTI in human post mortem tissue

Diffusion tensor imaging (DTI) is amongst the simplest mathematical models available for diffusion magnetic resonance imaging, yet still by far the most used one. Despite the success of DTI as an imaging tool for white matter fibers, its anatomical underpinnings on a microstructural basis remain unclear. In this study, we used 65 myelin-stained sections of human premotor cortex to validate modeled fiber orientations and oft used microstructure-sensitive scalar measures of DTI on the level of individual voxels. We performed this validation on high spatial resolution diffusion MRI acquisitions investigating both white and gray matter. We found a very good agreement between DTI and myelin orientations with the majority of voxels showing angular differences less than 10°. The agreement was strongest in white matter, particularly in unidirectional fiber pathways. In gray matter, the agreement was good in the deeper layers highlighting radial fiber directions even at lower fractional anisotropy (FA) compared to white matter. This result has potentially important implications for tractography algorithms applied to high resolution diffusion MRI data if the aim is to move across the gray/white matter boundary. We found strong relationships between myelin microstructure and DTI-based microstructure-sensitive measures. High FA values were linked to high myelin density and a sharply tuned histological orientation profile. Conversely, high values of mean diffusivity (MD) were linked to bimodal or diffuse orientation distributions and low myelin density. At high spatial resolution, DTI-based measures can be highly sensitive to white and gray matter microstructure despite being relatively unspecific to concrete microarchitectural aspects

Erratum to: Processing Time Reduction: an Application in Living Human High-Resolution Diffusion Magnetic Resonance Imaging Data (Journal of Medical Systems, (2016), 40, 11, (243), 10.1007/s10916-016-0594-2)

O artigo encontra-se disponível em: http://hdl.handle.net/1822/52841The original version of this article unfortunately contained an error. The correct spelling of Augustin Ibañez should be Agustin Ibañez.info:eu-repo/semantics/publishedVersio

Automatic atlas-based segmentation of brain white matter in neonates at risk for neurodevelopmental disorders

Very preterm infants, < 32 weeks gestation, are at high risk for brain injury. Cognitive deficits are often diagnosed at a later stage, since there are no available predictive biomarkers in the neonatal period. The maturation of specific white matter (WM) brain structures is considered a promising early-stage biomarker. With Diffusion Tensor Imaging (DTI) tractography, an in vivo and non-invasive evaluation of these anatomical structures is possible. We developed an automatic tractography segmentation pipeline, which allows for maturation assessment of the different segmented WM structures. Our segmentation pipeline is atlas-based, specifically designed for premature neonates at term equivalent age. In order to better make use of global information from tractography, all processing is done in the fiber domain. Segmented fiber bundles are further automatically quantified with respect to volume and anisotropy. Of the 24 automatically segmented neonatal tractographies, only three contained more than 30% mislabeled fibers. Results show no dependency to WM pathology. By automatically segmenting WM, we reduced the user-dependency and bias characteristic of manual methods. This study assesses the structure of the neonatal brain based on an automatic WM segmentation in the fiber domain method using DTI tractography data

Entropy and organizational performance

The main purpose of this article is to analyze the impact of the workers’ behavior in terms of their emotions and feelings in system’s performance, i.e., one is looking at issues concerned with Organizational Sustainability. Indeed, one’s aim is to define a process that motivates and inspires managers and personnel to act upon the limit, i.e., to achieve the organizational goals through an effective and efficient implementation of operational and behavioral strategies. The focus will be on the importance of specific psychosocial variables that may affect collective pro-organizational attitudes. Data that is increasing exponentially, and somehow being out of control, i.e., the question is to know the correct value of the information that may be behind these numbers.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019

Processing time reduction: an application in living human high-resolution diffusion magnetic resonance imaging data

Um errata deste artigo encontra-se disponível em: http://hdl.handle.net/1822/52993High Angular Resolution Diffusion Imaging (HARDI) is a type of brain imaging that collects a very large amount of data, and if many subjects are considered then it amounts to a big data framework (e.g., the human connectome project has 20 Terabytes of data). HARDI is also becoming increasingly relevant for clinical settings (e.g., detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter-WM anatomy using tractography). Thus, this method is becoming a routine assessment in clinical settings. In such settings, the computation time is critical, and finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI), a form of HARDI data, is very relevant to increase data-processing speed. Here we analyze a method for reducing the computation time of the dMRI-based axonal orientation distribution function h by using Monte Carlo sampling-based methods for voxel selection. Results evidenced a robust reduction in required data sampling of about 50 % without losing signal’s quality. Moreover, we show that the convergence to the correct value in this type of Monte Carlo HARDI/DSI data-processing has a linear improvement in data-processing speed of the ODF determination. Although further improvements are needed, our results represent a promissory step for future processing time reduction in big data.We thank the financial support by QREN, FEDER, COMPETE, Investigador FCT, FCT Ciencia 2007, FCT PTDC/SAU-BEB/100147/2008, FCT Project Scope UID/CEC/00319/2013, and the ERASMUS projects (FCT stands for "Fundacao para a Ciencia e Tecnologia"). We are thankful the relevant scientific conversations with Alard Roebroeck, Rainer Goebel, Van Wedeen, and Gina Caetano. Data collection for this work was in part from "Human Connectome Project" (HCP; Principal Investigators: Bruce Rosen, M.D., Ph.D., Arthur W. Toga, Ph.D., Van J. Weeden, MD). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California.info:eu-repo/semantics/publishedVersio