The Implementation of Governance Risk and Compliance Information Systems (GRC IS): Adoption Lifecycle and Enterprise Value

Governance, Risk and Compliance (GRC) has become an emerging field within the IS academic community. Motivated by this research direction, the study capitalizes on the theoretical background of Enterprise Systems (ES) and extends the focus on GRC systems’ implementation (enterprise value and lifecycle). Building upon expert views on GRC IS implementation projects, the analysis indicates that the three value drivers of integration; optimization and information should be considered throughout the whole GRC IS implementation lifecycle

Making sense of business analytics : the case of two start-ups

Business analytics have enabled businesses to leverage unstructured and dispersed data in order to improve their operations and position themselves better within a highly turbulent environment. While much discussion has been focused on how businesses can move from data to insights to decision making, much less is known around how businesses actually interpret the insights provided by business analytics tools. This extended abstract proposes the use of sense-making as the theoretical lens for interpreting these insights, combined with contextual information. We will be using two case studies to further explore the applicability of our proposition

Comparing Machine Learning and Deep Learning Techniques for Text Analytics: Detecting the Severity of Hate Comments Online

Data Availability: The data used in this work is a public dataset.Copyright © The Author(s) 2023. Social media platforms have become an increasingly popular tool for individuals to share their thoughts and opinions with other people. However, very often people tend to misuse social media posting abusive comments. Abusive and harassing behaviours can have adverse effects on people's lives. This study takes a novel approach to combat harassment in online platforms by detecting the severity of abusive comments, that has not been investigated before. The study compares the performance of machine learning models such as Naïve Bayes, Random Forest, and Support Vector Machine, with deep learning models such as Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM). Moreover, in this work we investigate the effect of text pre-processing on the performance of the machine and deep learning models, the feature set for the abusive comments was made using unigrams and bigrams for the machine learning models and word embeddings for the deep learning models. The comparison of the models’ performances showed that the Random Forest with bigrams achieved the best overall performance with an accuracy of (0.94), a precision of (0.91), a recall of (0.94), and an F1 score of (0.92). The study develops an efficient model to detect severity of abusive language in online platforms, offering important implications both to theory and practice.This research didn’t use any fund (public or private)

SORL1 mutation in a Greek family with Parkinson's disease and dementia

Whole exome sequencing and linkage analysis were performed in a three generational pedigree of Greek origin with a broad phenotypic spectrum spanning from Parkinson’s disease and Parkinson’s disease dementia to dementia of mixed type (Alzheimer disease and vascular dementia). We identified a novel heterozygous c.G1135T (p.G379W) variant in SORL1 which segregated with the disease in the family. Mutation screening in sporadic Greek PD cases identified one additional individual with the mutation, sharing the same 12.8Mb haplotype. Our findings provide support for SORL1 mutations resulting in a broad range of additional phenotypes and warrants further studies in neurodegenerative diseases beyond AD

Electroencephalographic source imaging: a prospective study of 152 operated epileptic patients

Electroencephalography is mandatory to determine the epilepsy syndrome. However, for the precise localization of the irritative zone in patients with focal epilepsy, costly and sometimes cumbersome imaging techniques are used. Recent small studies using electric source imaging suggest that electroencephalography itself could be used to localize the focus. However, a large prospective validation study is missing. This study presents a cohort of 152 operated patients where electric source imaging was applied as part of the pre-surgical work-up allowing a comparison with the results from other methods. Patients (n = 152) with >1 year postoperative follow-up were studied prospectively. The sensitivity and specificity of each imaging method was defined by comparing the localization of the source maximum with the resected zone and surgical outcome. Electric source imaging had a sensitivity of 84% and a specificity of 88% if the electroencephalogram was recorded with a large number of electrodes (128–256 channels) and the individual magnetic resonance image was used as head model. These values compared favourably with those of structural magnetic resonance imaging (76% sensitivity, 53% specificity), positron emission tomography (69% sensitivity, 44% specificity) and ictal/interictal single-photon emission-computed tomography (58% sensitivity, 47% specificity). The sensitivity and specificity of electric source imaging decreased to 57% and 59%, respectively, with low number of electrodes (<32 channels) and a template head model. This study demonstrated the validity and clinical utility of electric source imaging in a large prospective study. Given the low cost and high flexibility of electroencephalographic systems even with high channel counts, we conclude that electric source imaging is a highly valuable tool in pre-surgical epilepsy evaluation

A homozygous MED11 C-terminal variant causes a lethal neurodegenerative disease

Purpose: The mediator (MED) multisubunit-complex modulates the activity of the transcriptional machinery, and genetic defects in different MED subunits (17, 20, 27) have been implicated in neurologic diseases. In this study, we identified a recurrent homozygous variant in MED11 (c.325C>T; p.Arg109Ter) in 7 affected individuals from 5 unrelated families. Methods: To investigate the genetic cause of the disease, exome or genome sequencing were performed in 5 unrelated families identified via different research networks and Matchmaker Exchange. Deep clinical and brain imaging evaluations were performed by clinical pediatric neurologists and neuroradiologists. The functional effect of the candidate variant on both MED11 RNA and protein was assessed using reverse transcriptase polymerase chain reaction and western blotting using fibroblast cell lines derived from 1 affected individual and controls and through computational approaches. Knockouts in zebrafish were generated using clustered regularly interspaced short palindromic repeats/Cas9. Results: The disease was characterized by microcephaly, profound neurodevelopmental impairment, exaggerated startle response, myoclonic seizures, progressive widespread neurodegeneration, and premature death. Functional studies on patient-derived fibroblasts did not show a loss of protein function but rather disruption of the C-terminal of MED11, likely impairing binding to other MED subunits. A zebrafish knockout model recapitulates key clinical phenotypes. Conclusion: Loss of the C-terminal of MED subunit 11 may affect its binding efficiency to other MED subunits, thus implicating the MED-complex stability in brain development and neurodegeneration

PDXK mutations cause polyneuropathy responsive to pyridoxal 5'-phosphate supplementation.

OBJECTIVE: To identify disease-causing variants in autosomal recessive axonal polyneuropathy with optic atrophy and provide targeted replacement therapy. METHODS: We performed genome-wide sequencing, homozygosity mapping, and segregation analysis for novel disease-causing gene discovery. We used circular dichroism to show secondary structure changes and isothermal titration calorimetry to investigate the impact of variants on adenosine triphosphate (ATP) binding. Pathogenicity was further supported by enzymatic assays and mass spectroscopy on recombinant protein, patient-derived fibroblasts, plasma, and erythrocytes. Response to supplementation was measured with clinical validated rating scales, electrophysiology, and biochemical quantification. RESULTS: We identified biallelic mutations in PDXK in 5 individuals from 2 unrelated families with primary axonal polyneuropathy and optic atrophy. The natural history of this disorder suggests that untreated, affected individuals become wheelchair-bound and blind. We identified conformational rearrangement in the mutant enzyme around the ATP-binding pocket. Low PDXK ATP binding resulted in decreased erythrocyte PDXK activity and low pyridoxal 5'-phosphate (PLP) concentrations. We rescued the clinical and biochemical profile with PLP supplementation in 1 family, improvement in power, pain, and fatigue contributing to patients regaining their ability to walk independently during the first year of PLP normalization. INTERPRETATION: We show that mutations in PDXK cause autosomal recessive axonal peripheral polyneuropathy leading to disease via reduced PDXK enzymatic activity and low PLP. We show that the biochemical profile can be rescued with PLP supplementation associated with clinical improvement. As B6 is a cofactor in diverse essential biological pathways, our findings may have direct implications for neuropathies of unknown etiology characterized by reduced PLP levels. ANN NEUROL 2019;86:225-240

Social media and sensemaking patterns in new product development: demystifying the customer sentiment

Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers’ emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers’ appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms

Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

The metaverse has the potential to extend the physical world using augmented and virtual reality technologies allowing users to seamlessly interact within real and simulated environments using avatars and holograms. Virtual environments and immersive games (such as, Second Life, Fortnite, Roblox and VRChat) have been described as antecedents of the metaverse and offer some insight to the potential socio-economic impact of a fully functional persistent cross platform metaverse. Separating the hype and “meta…” rebranding from current reality is difficult, as “big tech” paints a picture of the transformative nature of the metaverse and how it will positively impact people in their work, leisure, and social interaction. The potential impact on the way we conduct business, interact with brands and others, and develop shared experiences is likely to be transformational as the distinct lines between physical and digital are likely to be somewhat blurred from current perceptions. However, although the technology and infrastructure does not yet exist to allow the development of new immersive virtual worlds at scale - one that our avatars could transcend across platforms, researchers are increasingly examining the transformative impact of the metaverse. Impacted sectors include marketing, education, healthcare as well as societal effects relating to social interaction factors from widespread adoption, and issues relating to trust, privacy, bias, disinformation, application of law as well as psychological aspects linked to addiction and impact on vulnerable people. This study examines these topics in detail by combining the informed narrative and multi-perspective approach from experts with varied disciplinary backgrounds on many aspects of the metaverse and its transformational impact. The paper concludes by proposing a future research agenda that is valuable for researchers, professionals and policy makers alike