Knowledge Sharing and Knowledge Management System Avoidance: The Role of Knowledge Type and the Social Network in Bypassing an Organizational Knowledge Management System

Knowledge sharing is a difficult task for most organizations, and there are many reasons for this. In this article, we propose that the nature of the knowledge shared and an individual\u27s social network influence employees to find more value in person-to-person knowledge sharing, which could lead them to bypass the codified knowledge provided by a knowledge management system (KMS). We surveyed employees of a workman\u27s compensation board in Canada and used social network analysis and hierarchical linear modeling to analyze the data. The results show that knowledge complexity and knowledge teachability increased the likelihood of finding value in person-to-person knowledge transfer, but knowledge observability did not. Contrary to expectations, whether the knowledge was available in the KMS had no impact on the value of person-to-person knowledge transfer. In terms of the social network, individuals with larger networks tended to perceive more value in the person-to-person transfer of knowledge than those with smaller networks

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Tracking smell loss to identify healthcare workers with SARS-CoV-2 infection

Introduction Healthcare workers (HCW) treating COVID-19 patients are at high risk for infection and may also spread infection through their contact with vulnerable patients. Smell loss has been associated with SARS-CoV-2 infection, but it is unknown whether monitoring for smell loss can be used to identify asymptomatic infection among high risk individuals. In this study we sought to determine if tracking smell sensitivity and loss using an at-home assessment could identify SARS-CoV-2 infection in HCW. Methods and findings We performed a prospective cohort study tracking 473 HCW across three months to determine if smell loss could predict SARS-CoV-2 infection in this high-risk group. HCW subjects completed a longitudinal, behavioral at-home assessment of olfaction with household items, as well as detailed symptom surveys that included a parosmia screening questionnaire, and real-time quantitative polymerase chain reaction testing to identify SARS-CoV-2 infection. Our main measures were the prevalence of smell loss in SARS-CoV-2-positive HCW versus SARS-CoV- 2-negative HCW, and timing of smell loss relative to SARS-CoV-2 test positivity. SARS-CoV-2 was identified in 17 (3.6%) of 473 HCW. HCW with SARS-CoV-2 infection were more likely to report smell loss than SARS-CoV-2-negative HCW on both the at-home assessment and the screening questionnaire (9/17, 53% vs 105/456, 23%, P < .01). 6/9 (67%) of SARS-CoV-2-positive HCW reporting smell loss reported smell loss prior to having a positive SARS-CoV-2 test, and smell loss was reported a median of two days before testing positive. Neurological symptoms were reported more frequently among SARS-CoV-2-positive HCW who reported smell loss compared to those without smell loss (9/9, 100% vs 3/8, 38%, P < .01). Conclusions In this prospective study of HCW, self-reported changes in smell using two different measures were predictive of SARS-CoV-2 infection. Smell loss frequently preceded a positive test and was associated with neurological symptoms

Megalin/LRP2 Expression Is Induced by Peroxisome Proliferator-Activated Receptor -Alpha and -Gamma: Implications for PPARs' Roles in Renal Function

BACKGROUND: Megalin is a large endocytic receptor with relevant functions during development and adult life. It is expressed at the apical surface of several epithelial cell types, including proximal tubule cells (PTCs) in the kidney, where it internalizes apolipoproteins, vitamins and hormones with their corresponding carrier proteins and signaling molecules. Despite the important physiological roles of megalin little is known about the regulation of its expression. By analyzing the human megalin promoter, we found three response elements for the peroxisomal proliferator-activated receptor (PPAR). The objective of this study was to test whether megalin expression is regulated by the PPARs. METHODOLOGY/PRINCIPAL FINDINGS: Treatment of epithelial cell lines with PPARα or PPARγ ligands increased megalin mRNA and protein expression. The stimulation of megalin mRNA expression was blocked by the addition of specific PPARα or PPARγ antagonists. Furthermore, PPAR bound to three PPAR response elements located in the megalin promoter, as shown by EMSA, and PPARα and its agonist activated a luciferase construct containing a portion of the megalin promoter and the first response element. Accordingly, the activation of PPARα and PPARγ enhanced megalin expression in mouse kidney. As previously observed, high concentrations of bovine serum albumin (BSA) decreased megalin in PTCs in vitro; however, PTCs pretreated with PPARα and PPARγ agonists avoided this BSA-mediated reduction of megalin expression. Finally, we found that megalin expression was significantly inhibited in the PTCs of rats that were injected with BSA to induce tubulointerstitial damage and proteinuria. Treatment of these rats with PPARγ agonists counteracted the reduction in megalin expression and the proteinuria induced by BSA. CONCLUSIONS: PPARα/γ and their agonists positively control megalin expression. This regulation could have an important impact on several megalin-mediated physiological processes and on pathophysiologies such as chronic kidney disease associated with diabetes and hypertension, in which megalin expression is impaired

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives