User-is Partnership And Is Development Success

Since 1970, high project failure rate and low user satisfaction has elicited research on users and their role in the process. It is believed that users\u27 physical participation or psychological involvement in the development process can improve user satisfaction and/or system quality. Previous research treats users as a source of requirements and hypothesizes satisfaction to increase when requirements are fulfilled. However, inconsistent conclusions lead to confusion. Recently, a co-production concept has been proposed to understand consumer participation in product development process. In this reconceptualization, users, instead of requirement generator, should be part of the production. In this study, based on co-production concept, we view users as one knowledge source and study how knowledge can be coordinated through the co-production process. After collecting data from 97 system users, most of the hypothesized relationships have been confirmed. IS-user co-production has a positive effect on expertise coordination and, in turn, improves teamwork outcomes. The only relationship that is not significant is between bring expertise to bear and creativity. Implications for practitioner and suggestion for future research are provided. Co-production was found to be a second-order construct comprised of multiple formative constructs. Higher levels of coproduction behavior were expected and were found to produce better outcomes of collaborative efforts. For future study, this relationship is expected to hold true when pairs of information systems developers and information systems users who have worked together on the same information systems development project are surveyed at the end of their projects (or just before it ends or recently thereafter)

Fostering Critical Thinking, Reasoning, and Argumentation Skills through Bioethics Education

Developing a position on a socio-scientific issue and defending it using a well-reasoned justification involves complex cognitive skills that are challenging to both teach and assess. Our work centers on instructional strategies for fostering critical thinking skills in high school students using bioethical case studies, decision-making frameworks, and structured analysis tools to scaffold student argumentation. In this study, we examined the effects of our teacher professional development and curricular materials on the ability of high school students to analyze a bioethical case study and develop a strong position. We focused on student ability to identify an ethical question, consider stakeholders and their values, incorporate relevant scientific facts and content, address ethical principles, and consider the strengths and weaknesses of alternate solutions. 431 students and 12 teachers participated in a research study using teacher cohorts for comparison purposes. The first cohort received professional development and used the curriculum with their students; the second did not receive professional development until after their participation in the study and did not use the curriculum. In order to assess the acquisition of higher-order justification skills, students were asked to analyze a case study and develop a well-reasoned written position. We evaluated statements using a scoring rubric and found highly significant differences (p<0.001) between students exposed to the curriculum strategies and those who were not. Students also showed highly significant gains (p<0.001) in self-reported interest in science content, ability to analyze socio-scientific issues, awareness of ethical issues, ability to listen to and discuss viewpoints different from their own, and understanding of the relationship between science and society. Our results demonstrate that incorporating ethical dilemmas into the classroom is one strategy for increasing student motivation and engagement with science content, while promoting reasoning and justification skills that help prepare an informed citizenry

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

Positive psychology of Malaysian students: impacts of engagement, motivation, self-compassion and wellbeing on mental health

Malaysia plays a key role in education of the Asia Pacific, expanding its scholarly output rapidly. However, mental health of Malaysian students is challenging, and their help-seeking is low because of stigma. This study explored the relationships between mental health and positive psychological constructs (academic engagement, motivation, self-compassion, and wellbeing), and evaluated the relative contribution of each positive psychological construct to mental health in Malaysian students. An opportunity sample of 153 students completed the measures regarding these constructs. Correlation, regression, and mediation analyses were conducted. Engagement, amotivation, self-compassion, and wellbeing were associated with, and predicted large variance in mental health. Self-compassion was the strongest independent predictor of mental health among all the positive psychological constructs. Findings can imply the strong links between mental health and positive psychology, especially selfcompassion. Moreover, intervention studies to examine the effects of self-compassion training on mental health of Malaysian students appear to be warranted.N/

Analysis of Signaling Mechanisms Regulating Microglial Process Movement

Microglia, the brain’s innate immune cells, are extremely motile cells, continuously surveying the CNS to serve homeostatic functions and to respond to pathological events. In the healthy brain, microglia exhibit a small cell body with long, branched and highly motile processes, which constantly extend and retract, effectively ‘patrolling’ the brain parenchyma. Over the last decade, methodological advances in microscopy and the availability of genetically encoded reporter mice have allowed us to probe microglial physiology in situ. Beyond their classical immunological roles, unexpected functions of microglia have been revealed, both in the developing and the adult brain: microglia regulate the generation of newborn neurons, control the formation and elimination of synapses, and modulate neuronal activity. Many of these newly ascribed functions depend directly on microglial process movement. Thus, elucidating the mechanisms underlying microglial motility is of great importance to understand their role in brain physiology and pathophysiology. Two-photon imaging of fluorescently labelled microglia, either in vivo or ex vivo in acute brain slices, has emerged as an indispensable tool for investigating microglial movements and their functional consequences. This chapter aims to provide a detailed description of the experimental data acquisition and analysis needed to address these questions, with a special focus on key dynamic and morphological metrics such as surveillance, directed motility and ramification

Improving Care of Patients At-Risk for Osteoporosis: A Randomized Controlled Trial

BACKGROUND: Despite accurate diagnostic tests and effective therapies, the management of osteoporosis has been observed to be suboptimal in many settings. We tested the effectiveness of an intervention to improve care in patients at-risk of osteoporosis. DESIGN: Randomized controlled trial. PARTICIPANTS: Primary care physicians and their patients at-risk of osteoporosis, including women 65 years and over, men and women 45 and over with a prior fracture, and men and women 45 and over who recently used ≥90 days of oral glucocorticoids. INTERVENTION: A multifaceted program of education and reminders delivered to primary care physicians as well as mailings and automated telephone calls to patients. Outcome: Either undergoing a bone mineral density (BMD) testing or filling a prescription for a bone-active medication during the 10 months of follow-up. RESULTS: After the intervention, 144 (14%) patients in the intervention group and 97 (10%) patients in the control group received either a BMD test or filled a prescription for an osteoporosis medication. This represents a 4% absolute increase and a 45% relative increase (95% confidence interval 9–93%, p = 0.01) in osteoporosis management between the intervention and control groups. No differences between groups were observed in the incidence of fracture. CONCLUSION: An intervention targeting primary care physicians and their at-risk patients increased the frequency of BMD testing and/or filling prescriptions for osteoporosis medications. However, the absolute percentage of at-risk patients receiving osteoporosis management remained low

UPF1, a Conserved Nonsense-Mediated mRNA Decay Factor, Regulates Cyst Wall Protein Transcripts in Giardia lamblia

The Giardia lamblia cyst wall is required for survival outside the host and infection. Three cyst wall protein (cwp) genes identified to date are highly up-regulated during encystation. However, little is known of the molecular mechanisms governing their gene regulation. Messenger RNAs containing premature stop codons are rapidly degraded by a nonsense-mediated mRNA decay (NMD) system to avoid production of non-functional proteins. In addition to RNA surveillance, NMD also regulates thousands of naturally occurring transcripts through a variety of mechanisms. It is interesting to know the NMD pathway in the primitive eukaryotes. Previously, we have found that the giardial homologue of a conserved NMD factor, UPF1, may be functionally conserved and involved in NMD and in preventing nonsense suppression. In this study, we tested the hypothesis that NMD factors can regulate some naturally occurring transcripts in G. lamblia. We found that overexpression of UPF1 resulted in a significant decrease of the levels of CWP1 and cyst formation and of the endogenous cwp1-3, and myb2 mRNA levels and stability. This indicates that NMD could contribute to the regulation of the cwp1-3 and myb2 transcripts, which are key to G. lamblia differentiation into cyst. Interestingly, we also found that UPF1 may be involved in regulation of eight other endogenous genes, including up-regulation of the translation elongation factor gene, whose product increases translation which is required for NMD. Our results indicate that NMD factor could contribute to the regulation of not only nonsense containing mRNAs, but also mRNAs of the key encystation-induced genes and other endogenous genes in the early-diverging eukaryote, G. lamblia

Discovery of Novel Hypermethylated Genes in Prostate Cancer Using Genomic CpG Island Microarrays

BACKGROUND: Promoter and 5' end methylation regulation of tumour suppressor genes is a common feature of many cancers. Such occurrences often lead to the silencing of these key genes and thus they may contribute to the development of cancer, including prostate cancer. METHODOLOGY/PRINCIPAL FINDINGS: In order to identify methylation changes in prostate cancer, we performed a genome-wide analysis of DNA methylation using Agilent human CpG island arrays. Using computational and gene-specific validation approaches we have identified a large number of potential epigenetic biomarkers of prostate cancer. Further validation of candidate genes on a separate cohort of low and high grade prostate cancers by quantitative MethyLight analysis has allowed us to confirm DNA hypermethylation of HOXD3 and BMP7, two genes that may play a role in the development of high grade tumours. We also show that promoter hypermethylation is responsible for downregulated expression of these genes in the DU-145 PCa cell line. CONCLUSIONS/SIGNIFICANCE: This study identifies novel epigenetic biomarkers of prostate cancer and prostate cancer progression, and provides a global assessment of DNA methylation in prostate cancer

3D Protein structure prediction with genetic tabu search algorithm

Abstract Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively