Exploring the dimensions of IT-based Organizational Learning Ability

This paper reports the results of an exploratory study aimed at identifying the underlying dimensions of a complex concept known as organizational learning ability (OLA). Understanding OLA is important because it is a vital part for explaining why firms are sometimes reluctant to accept and use new technologies with confidence. A good understanding of OLA is also essential to the development of a valid and reliable OLA measurement instrument. Such an instrument, in the context of information technology adoption, does not exist yet. Drawing from the prior literature and several focus group interviews, 42 items purporting to measure OLA were generated. A questionnaire developed from these 42 items was administered to 1200 firms, yielding a response rate of around 15%. The responses were subjected to an exploratory factor analysis, indicating that the co-variation among the proposed OLA 42 items can be explained by a correlated six-factor measurement which may be interpreted as: adaptability, compatibility, accessibility, information management, awareness and unit support. This study not only contributes to a better understanding of the OL concept, but also provides a basis for the construction of an instrument for measuring OLA

Diabetes Mellitus Type 2 as a Risk Factor and Outcome Modifier for Cryptococcosis in HIV Negative, Non-transplant Patients, a Propensity Score Match Analysis

Cryptococcosis is an opportunistic fungal infection of worldwide distribution with significant associated morbidity and mortality. HIV, organ transplantation, malignancy, cirrhosis, sarcoidosis, and immunosuppressive medications are established risk factors for cryptococcosis. Type 2 diabetes mellitus (DM2) has been hypothesized as a risk factor and an outcome modifier for cryptococcosis. We aimed to compare outcomes among HIV-negative, non-transplant (NHNT) patients with and without DM2. We queried a global research network to identify NHNT patients (n = 3280). We performed a propensity score-matched (PSM) analysis comparing clinical outcomes among cryptococcosis patients by DM status. We also characterize adults with cryptococcosis and DM2 as the only risk factor. After PSM, NHNT patients with DM2 were more likely to develop cognitive dysfunction [9% vs. 6%, OR 1.6; 95% CI (1.1–2.3); P = 0.01] but had similar mortality, hospitalization, ICU, and stroke risk after acquiring cryptococcosis when compared to NHNT patients without DM2. Pulmonary cryptococcosis was the most common site of infection. Among 44 cryptococcosis patients with DM2 as the only identifiable risk factor for disease, the annual incidence of cryptococcosis was 0.001%, with a prevalence of 0.002%. DM2 is associated with increased cognitive dysfunction risk in NHNT patients with cryptococcosis. It is rare for DM2 to be the only identified risk factor for developing cryptococcosis. Kidney disease, hyperglycemia, and immune dysfunction can increase the risk of cryptococcosis in patients with DM2

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable

Organizational Learning Ability in the Context of Advanced IT Adoption

Information technology (IT) can help organizations achieve competitive advantages. Yet many organizations are slow to adopt IT. To explain this phenomenon a line of research has arisen, identifying the relationship between the perceived attributes of an IT and its adoption by organizations (Rogers, 1995). In particular, these perceived attributes have been found to influence the adoption rate directly. However, these perceived attributes alone are not sufficient to predict the adoption rate of IT in organizations. Attewell (1992) has pointed out that organizational learning ability is another important but often neglected factor predicting adoption. However, this hypothesis needs to be tested with empirical data. Unfortunately, there is no reliable and validated instrument available in the literature for measuring organizational learning (OL) ability in the context of IT adoption. The task of constructing such an instrument is made more difficult due to the fact that OL is a complex and abstract phenomenon most likely embodying multiple dimensions. Without first establishing the underlying dimensions of this phenomenon, it would be difficult to come up with a valid instrument to measure OL ability. Drawing from the OL literature, much of it is qualitative, and based on a process view of OL, this paper analyses the OL concept and proposes a few plausible underlying dimensions for OL ability. These dimensions are: awareness, accessibility, compatibility, codifiability and adaptability. The paper contributes to a better understanding of the OL concept and provides some clues to the construction of an instrument for measuring OL ability

A prescription for clinical immunology: the pills are available and ready for testing. A review

Modern immunology has been extremely successful in elucidating many features of the immune system, but not in stemming pandemics of non-infectious, immune-related disease associated with industrialized populations. These pandemics involve a broad range of allergic, autoimmune, and inflammatory diseases, potentially including neuroinflammatory-associated disorders. It is the purpose of this review to outline the literature pointing toward the causes and potential treatments of these problems

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3–7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium.

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable

Altered white matter microstructural organization in posttraumatic stress disorder across 3047 adults: results from the PGC-ENIGMA PTSD consortium

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1426 individuals with PTSD and 1621 controls (2174 males/873 females) between ages 18-83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen&#39;s d = -0.11, p = 0.0055). The tapetum connects the left and right hippocampus, for which structure and function have been consistently implicated in PTSD. Results were consistent even after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.</p

Altered white matter microstructural organization in posttraumatic stress disorder across 3047 adults: results from the PGC-ENIGMA PTSD consortium

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1426 individuals with PTSD and 1621 controls (2174 males/873 females) between ages 18–83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen’s d = −0.11, p = 0.0055). The tapetum connects the left and right hippocampus, for which structure and function have been consistently implicated in PTSD. Results were consistent even after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network