Adaptive notifications to support knowledge sharing in virtual communities

Social web-groups where people with common interests and goals communicate, share resources, and construct knowledge, are becoming a major part of today’s organisational practice. Research has shown that appropriate support for effective knowledge sharing tailored to the needs of the community is paramount. This brings a new challenge to user modelling and adaptation, which requires new techniques for gaining sufficient understanding of a virtual community (VC) and identifying areas where the community may need support. The research presented here addresses this challenge presenting a novel computational approach for community-tailored support underpinned by organisational psychology and aimed at facilitating the functioning of the community as a whole (i.e. as an entity). A framework describing how key community processes—transactive memory (TM), shared mental models (SMMs), and cognitive centrality (CCen)—can be utilised to derive knowledge sharing patterns from community log data is described. The framework includes two parts: (i) extraction of a community model that represents the community based on the key processes identified and (ii) identification of knowledge sharing behaviour patterns that are used to generate adaptive notifications. Although the notifications target individual members, they aim to influence individuals’ behaviour in a way that can benefit the functioning of the community as a whole. A validation study has been performed to examine the effect of community-adapted notifications on individual members and on the community as a whole using a close-knit community of researchers sharing references. The study shows that notification messages can improve members’ awareness and perception of how they relate to other members in the community. Interesting observations have been made about the linking between the physical and the VC, and how this may influence members’ awareness and knowledge sharing behaviour. Broader implications for using log data to derive community models based on key community processes and generating community-adapted notifications are discussed

Probing metal ion binding and conformational properties of the colicin E9 endonuclease by electrospray ionization time-of-flight mass spectrometry

Nano-electrospray ionization time-of-flight mass spectrometry (ESI-MS) was used to study the conformational consequences of metal ion binding to the colicin E9 endonuclease (E9 DNase) by taking advantage of the unique capability of ESI-MS to allow simultaneous assessment of conformational heterogeneity and metal ion binding. Alterations of charge state distributions on metal ion binding/release were correlated with spectral changes observed in far- and near-UV circular dichroism (CD) and intrinsic tryptophan fluorescence. In addition, hydrogen/deuterium (H/D) exchange experiments were used to probe structural integrity. The present study shows that ESI-MS is sensitive to changes of the thermodynamic stability of E9 DNase as a result of metal ion binding/release in a manner consistent with that deduced from proteolysis and calorimetric experiments. Interestingly, acid-induced release of the metal ion from the E9 DNase causes dramatic conformational instability associated with a loss of fixed tertiary structure, but secondary structure is retained. Furthermore, ESI-MS enabled the direct observation of the noncovalent protein complex of E9 DNase bound to its cognate immunity protein Im9 in the presence and absence of Zn2+. Gas-phase dissociation experiments of the deuterium-labeled binary and ternary complexes revealed that metal ion binding, not Im9, results in a dramatic exchange protection of E9 DNase in the complex. In addition, our metal ion binding studies and gas-phase dissociation experiments of the ternary E9 DNase-Zn2+-Im9 complex have provided further evidence that electrostatic interactions govern the gas phase ion stability

Lessons learned using a virtual world to support collaborative learning in the classroom

© 2020, IICM. All rights reserved. Using technology in education is crucial to support learning, and Virtual Worlds (VWs) are one of the technologies used by many educators to support their teaching objectives. VWs enable students to connect, synchronously interact, and participate in immersive learning activities. Such VW has been developed at Sheffield Hallam University (UK), and is used to support the teaching of a specific module, as well as for conducting empirical research around the topics of Transactive Memory Systems (TMS) and Students Engagement. TMS is a phenomenon representing the collective awareness of a group's specialisation, coordination, and credibility with interesting results. This paper presents the lessons learned while using the VW over the past few years at a higher education institution to support collaborative learning within working groups. A review of these empirical findings is presented, together with the results of a follow up study conducted to further investigate TMS and student Engagement, as well as students perceived Motivation to use a VW for learning, and their Learning Outcomes. The findings of this study are corroborating and contributing to previous results, suggesting that a VW is an effective tool to support collaborative learning activities, allowing students to engage in the learning process, motivate them to participate in activities, and contribute to their overall learning experience

Ferredoxin containing bacteriocins suggest a novel mechanism of iron uptake in <i>Pectobacterium spp</i>

In order to kill competing strains of the same or closely related bacterial species, many bacteria produce potent narrow-spectrum protein antibiotics known as bacteriocins. Two sequenced strains of the phytopathogenic bacterium &lt;i&gt;Pectobacterium carotovorum&lt;/i&gt; carry genes encoding putative bacteriocins which have seemingly evolved through a recombination event to encode proteins containing an N-terminal domain with extensive similarity to a [2Fe-2S] plant ferredoxin and a C-terminal colicin M-like catalytic domain. In this work, we show that these genes encode active bacteriocins, pectocin M1 and M2, which target strains of &lt;i&gt;Pectobacterium carotovorum&lt;/i&gt; and &lt;i&gt;Pectobacterium atrosepticum&lt;/i&gt; with increased potency under iron limiting conditions. The activity of pectocin M1 and M2 can be inhibited by the addition of spinach ferredoxin, indicating that the ferredoxin domain of these proteins acts as a receptor binding domain. This effect is not observed with the mammalian ferredoxin protein adrenodoxin, indicating that &lt;i&gt;Pectobacterium spp.&lt;/i&gt; carries a specific receptor for plant ferredoxins and that these plant pathogens may acquire iron from the host through the uptake of ferredoxin. In further support of this hypothesis we show that the growth of strains of &lt;i&gt;Pectobacterium carotovorum&lt;/i&gt; and &lt;i&gt;atrosepticum&lt;/i&gt; that are not sensitive to the cytotoxic effects of pectocin M1 is enhanced in the presence of pectocin M1 and M2 under iron limiting conditions. A similar growth enhancement under iron limiting conditions is observed with spinach ferrodoxin, but not with adrenodoxin. Our data indicate that pectocin M1 and M2 have evolved to parasitise an existing iron uptake pathway by using a ferredoxin-containing receptor binding domain as a Trojan horse to gain entry into susceptible cells

Reliability and psychometric properties of the Greek translation of the State-Trait Anxiety Inventory form Y: Preliminary data

BACKGROUND: The State-Trait Anxiety Inventory form Y is a brief self-rating scale for the assessment of state and trait anxiety. The aim of the current preliminary study was to assess the psychometric properties of its Greek translation. MATERIALS AND METHODS: 121 healthy volunteers 27.22 ± 10.61 years old, and 22 depressed patients 29.48 ± 9.28 years old entered the study. In 20 of them the instrument was re-applied 1–2 days later. Translation and Back Translation was made. The clinical diagnosis was reached with the SCAN v.2.0 and the IPDE. The Symptoms Rating Scale for Depression and Anxiety (SRSDA) and the EPQ were applied for cross-validation purposes. The Statistical Analysis included the Pearson Correlation Coefficient and the calculation of Cronbach's alpha. RESULTS: The State score for healthy subjects was 34.30 ± 10.79 and the Trait score was 36.07 ± 10.47. The respected scores for the depressed patients were 56.22 ± 8.86 and 53.83 ± 10.87. Both State and Trait scores followed the normal distribution in control subjects. Cronbach's alpha was 0.93 for the State and 0.92 for the Trait subscale. The Pearson Correlation Coefficient between State and Trait subscales was 0.79. Both subscales correlated fairly with the anxiety subscale of the SRSDA. Test-retest reliability was excellent, with Pearson coefficient being between 0.75 and 0.98 for individual items and equal to 0.96 for State and 0.98 for Trait. CONCLUSION: The current study provided preliminary evidence concerning the reliability and the validity of the Greek translation of the STAI-form Y. Its properties are generally similar to those reported in the international literature, but further research is necessary

Structures of the Ultra-High-Affinity Protein-Protein Complexes of Pyocins S2 and AP41 and Their Cognate Immunity Proteins from Pseudomonas aeruginosa

© 2015 The Authors. Published by Elsevier Ltd. How ultra-high-affinity protein-protein interactions retain high specificity is still poorly understood. The interaction between colicin DNase domains and their inhibitory immunity (Im) proteins is an ultra-high-affinity interaction that is essential for the neutralisation of endogenous DNase catalytic activity and for protection against exogenous DNase bacteriocins. The colicin DNase-Im interaction is a model system for the study of high-affinity protein-protein interactions. However, despite the fact that closely related colicin-like bacteriocins are widely produced by Gram-negative bacteria, this interaction has only been studied using colicins from Escherichia coli. In this work, we present the first crystal structures of two pyocin DNase-Im complexes from Pseudomonas aeruginosa, pyocin S2 DNase-ImS2 and pyocin AP41 DNase-ImAP41. These structures represent divergent DNase-Im subfamilies and are important in extending our understanding of protein-protein interactions for this important class of high-affinity protein complex. A key finding of this work is that mutations within the immunity protein binding energy hotspot, helix III, are tolerated by complementary substitutions at the DNase-Immunity protein binding interface. Im helix III is strictly conserved in colicins where an Asp forms polar interactions with the DNase backbone. ImAP41 contains an Asp-to-Gly substitution in helix III and our structures show the role of a co-evolved substitution where Pro in DNase loop 4 occupies the volume vacated and removes the unfulfilled hydrogen bond. We observe the co-evolved mutations in other DNase-Immunity pairs that appear to underpin the split of this family into two distinct groups

2&apos;-O-methoxyethyl splice-switching oligos correct splicing from IVS2-745 &#946;-thalassemia patient cells restoring HbA production and chain rebalance

\u3b2-thalassemia is a disorder caused by altered hemoglobin protein synthesis and affects individuals worldwide. Severe forms of the disease, left untreated, can result in death before the age of 3 years (1). The standard of care consists of chronic and costly palliative treatment by blood transfusion combined with iron chelation. This dual approach suppresses anemia and reduces iron-related toxicities in patients. Allogeneic bone marrow transplant is an option, but limited by the availability of a highly compatible HSC donor. While gene therapy is been explored in several trials, its use is highly limited to developed regions with centers of excellence and well-established healthcare systems (2). Hence, there remains a tremendous unmet medical need to develop alternative treatment strategies for \u3b2-thalassemia (3). Occurrence of aberrant splicing is one of the processes that affects \u3b2-globin synthesis in \u3b2-thalassemia. The (C&gt;G) IVS-2-745 is a splicing mutation within intron 2 of the \u3b2-globin gene. It leads to an aberrantly spliced mRNA that incorporates an intron fragment. This results in an in-frame premature termination codon that inhibits \u3b2-globin production. Here, we propose the use of uniform 2'-O-methoxyethyl (2'-MOE) splice switching oligos (SSOs) to reverse this aberrant splicing in the pre-mRNA. With these lead SSOs we show aberrant to wild type splice switching. This switching leads to an increase of adult hemoglobin (HbA) up to 80% in erythroid cells from patients with the IVS-2-745 mutation. Furthermore, we demonstrate a restoration of the balance between \u3b2-like- and \u3b1-globin chains, and up to an 87% reduction in toxic \u3b1-heme aggregates. While examining the potential benefit of 2'-MOE-SSOs in a mixed sickle-thalassemic phenotypic setting, we found reduced HbS synthesis and sickle cell formation due to HbA induction. In summary, 2'-MOE-SSOs are a promising therapy for forms of \u3b2-thalassemia caused by mutations leading to aberrant splicing

Feature Extraction and Random Forest to Identify Sheep Behavior from Accelerometer Data

Sensor technologies play an essential part in the agricultural community and many other scientific and commercial communities. Accelerometer signals and Machine Learning techniques can be used to identify and observe behaviours of animals without the need for an exhaustive human observation which is labour intensive and time consuming. This study employed random forest algorithm to identify grazing, walking, scratching, and inactivity (standing, resting) of 8 Hebridean ewes located in Cheshire, Shotwick in the UK. We gathered accelerometer data from a sensor device which was fitted on the collar of the animals. The selection of the algorithm was based on previous research by which random forest achieved the best results among other benchmark techniques. Therefore, in this study, more focus was given to feature engineering to improve prediction performance. Seventeen features from time and frequency domain were calculated from the accelerometer measurements and the magnitude of the acceleration. Feature elimination was utilised in which highly correlated ones were removed, and only nine out of seventeen features were selected. The algorithm achieved an overall accuracy of 99.43% and a kappa value of 98.66%. The accuracy for grazing, walking, scratching, and inactive was 99.08%, 99.13%, 99.90%, and 99.85%, respectively. The overall results showed that there is a significant improvement over previous methods and studies for all mutually exclusive behaviours. Those results are promising, and the technique could be further tested for future real-time activity recognition