The Effect of Socializing via Computer-mediated Communication on the Relationship between Organizational Culture and Organizational Creativity

An organization’s culture plays a strong role in its creating new knowledge, but, as organizations become more dispersed and technologies more advanced, many come to rely on computer-mediated communication (CMC) for employees to engage in all levels of knowledge management. Researchers have conducted little work to understand the effectiveness of socializing via CMC on organizational creativity, particularly as it relates to organizational culture. Some organizations tend toward a group culture, while others lean toward a rational culture. We investigate how both face-to-face (FTF) and computer-mediated socializing influence the relationship between organizational culture and organizational creativity at each cultural extreme. We surveyed 186 knowledge workers to investigate these relationships. Organizational culture interacted with socializing such that creativity in rational cultures benefited from using CMC to socialize, while group cultures appeared to be agnostic to different socializing types

Inter-species variation in the oligomeric states of the higher plant Calvin cycle enzymes glyceraldehyde-3-phosphate dehydrogenase and phosphoribulokinase

In darkened leaves the Calvin cycle enzymes glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and phosphoribulokinase (PRK) form a regulatory multi-enzyme complex with the small chloroplast protein CP12. GAPDH also forms a high molecular weight regulatory mono-enzyme complex. Given that there are different reports as to the number and subunit composition of these complexes and that enzyme regulatory mechanisms are known to vary between species, it was reasoned that protein-protein interactions may also vary between species. Here, this variation is investigated. This study shows that two different tetramers of GAPDH (an A2B2 heterotetramer and an A4 homotetramer) have the capacity to form part of the PRK/GAPDH/CP12 complex. The role of the PRK/GAPDH/CP12 complex is not simply to regulate the 'non-regulatory' A4 GAPDH tetramer. This study also demonstrates that the abundance and nature of PRK/GAPDH/CP12 interactions are not equal in all species and that whilst NAD enhances complex formation in some species, this is not sufficient for complex formation in others. Furthermore, it is shown that the GAPDH mono-enzyme complex is more abundant as a 2(A2B2) complex, rather than the larger 4(A2B2) complex. This smaller complex is sensitive to cellular metabolites indicating that it is an important regulatory isoform of GAPDH. This comparative study has highlighted considerable heterogeneity in PRK and GAPDH protein interactions between closely related species and the possible underlying physiological basis for this is discussed. © 2011 The Author(s)

Non-Markovian quantum state diffusion for absorption spectra of molecular aggregates

In many molecular systems one encounters the situation where electronic excitations couple to a quasi-continuum of phonon modes. That continuum may be highly structured e.g. due to some weakly damped high frequency modes. To handle such a situation, an approach combining the non-Markovian quantum state diffusion (NMQSD) description of open quantum systems with an efficient but abstract approximation was recently applied to calculate energy transfer and absorption spectra of molecular aggregates [Roden, Eisfeld, Wolff, Strunz, PRL 103 (2009) 058301]. To explore the validity of the used approximation for such complicated systems, in the present work we compare the calculated (approximative) absorption spectra with exact results. These are obtained from the method of pseudomodes, which we show to be capable of determining the exact spectra for small aggregates and a few pseudomodes. It turns out that in the cases considered, the results of the two approaches mostly agree quite well. The advantages and disadvantages of the two approaches are discussed

Training, Self-Efficacy, and Performance; a Replication Study

A conceptual replication of multiple prior IS studies was conducted with the aim of providing stronger empirical support for those results. Conducting six separate longitudinal studies, the effect of professional training on improving one’s application-specific computer self-efficacy (AS-CSE) was shown. Also in line with some prior IS studies it was shown that an application-specific measure of self-efficacy is better able to predict one’s performance in accomplishing tasks in the corresponding domain than a general computer self-efficacy (GCSE) measure. Moreover, it is shown that, regardless of the type and characteristics of the training method, individuals’ perceptions of quality of training significantly affects their AS-CSE after the training course

AI for predicting chemical-effect associations at the chemical universe level – deepFPlearn

Many chemicals are out there in our environment, and all living species are exposed. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods – even if high throughput – are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data.We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feedforward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful - experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds.We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.Supplementary information Supplementary data are available at bioRxiv online.Contact jana.schor{at}ufz.deCompeting Interest StatementThe authors have declared no competing interest

AI for predicting chemical-effect associations at the chemical universe level: DeepFPlearn

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn

Attitude shifts and knowledge gains: Evaluating men who have sex with men sensitisation training for healthcare workers in the Western Cape, South Africa

Background: Men who have sex with men (MSM) in South Africa experience discrimination from healthcare workers (HCWs), impeding health service access.Objectives: To evaluate the outcomes of an MSM sensitisation training programme for HCWs implemented in the Western Cape province (South Africa).Methods: A training programme was developed to equip HCWs with the knowledge, awareness and skills required to provide non-discriminatory, non-judgemental and appropriate services to MSM. Overall, 592 HCWs were trained between February 2010 and May 2012. Trainees completed self-administered pre- and post-training questionnaires assessing changes in knowledge. Two-sample t-tests for proportion were used to assess changes in specific answers and the Wilcoxon rank-sum test for overall knowledge scores. Qualitative data came from anonymous post-training evaluation forms completed by all trainees, in combination with four focus group discussions (n = 28) conducted six months after their training.Results: Fourteen per cent of trainees had received previous training to counsel clients around penile–anal intercourse, and 16% had previously received training around sexual health issues affecting MSM. There was a statistically significant improvement in overall knowledge scores (80% – 87%, p < 0.0001), specifically around penile–anal intercourse, substance use and depression after the training. Reductions in negative attitudes towards MSM and increased ability for HCWs to provide non-discriminatory care were reported as a result of the training.Conclusion: MSM sensitisation training for HCWs is an effective intervention to increase awareness on issues pertaining to MSM and how to engage around them, reduce discriminatory attitudes and enable the provision of non-judgemental and appropriate services by HCWs