The Cognitive Ecology of the Internet

In this chapter, we analyze the relationships between the Internet and its users in terms of situated cognition theory. We first argue that the Internet is a new kind of cognitive ecology, providing almost constant access to a vast amount of digital information that is increasingly more integrated into our cognitive routines. We then briefly introduce situated cognition theory and its species of embedded, embodied, extended, distributed and collective cognition. Having thus set the stage, we begin by taking an embedded cognition view and analyze how the Internet aids certain cognitive tasks. After that, we conceptualize how the Internet enables new kinds of embodied interaction, extends certain aspects of our embodiment, and examine how wearable technologies that monitor physiological, behavioral and contextual states transform the embodied self. On the basis of the degree of cognitive integration between a user and Internet resource, we then look at how and when the Internet extends our cognitive processes. We end this chapter with a discussion of distributed and collective cognition as facilitated by the Internet

A preliminary investigation of materialism and impulsiveness as predictors of technological addictions among young adults

Background and aims: The primary objective of the present research is to investigate the drivers of technological addiction in college students — heavy users of Information and Communication Technology (ICT). The study places cell phone and instant messaging addiction in the broader context of consumption pathologies, investigating the influence of materialism and impulsiveness on these two technologies. Clearly, cell phones serve more than just a utilitarian purpose. Cell phones are used in public and play a vital role in the lives of young adults. The accessibility of new technologies, like cell phones, which have the advantages of portability and an ever increasing array of functions, makes their over-use increasingly likely. Methods: College undergraduates (N = 191) from two U.S. universities completed a paper and pencil survey instrument during class. The questionnaire took approximately 15–20 minutes to complete and contained scales that measured materialism, impulsiveness, and mobile phone and instant messaging addiction. Results: Factor analysis supported the discriminant validity of Ehrenberg, Juckes, White and Walsh's (2008) Mobile Phone and Instant Messaging Addictive Tendencies Scale. The path model indicates that both materialism and impulsiveness impact the two addictive tendencies, and that materialism's direct impact on these addictions has a noticeably larger effect on cell phone use than instant messaging. Conclusions: The present study finds that materialism and impulsiveness drive both a dependence on cell phones and instant messaging. As Griffiths (2012) rightly warns, however, researchers must be aware that one's addiction may not simply be to the cell phone, but to a particular activity or function of the cell phone. The emergence of multi-function smart phones requires that research must dig beneath the technology being used to the activities that draw the user to the particular technology

Metabotyping of docosahexaenoic acid - Treated alzheimer's disease cell model

10.1371/journal.pone.0090123PLoS ONE92-POLN

Transformations structurelles et essor du métal

The role of multiattribute utility theory is first placed in the overall context of decision analysis. Then an approach that has proven useful in adapting the theory to be a practical tool is illustrated. Several cases where multiattribute utility has been used are briefly discussed. These include both operational and strategic problems involving, for example, siting of large-scale facilities (airports, power plants), medical treatment, the structuring corporate objectives, environmental management, and personal investment strategy

Docosahexaenoic Acid-Derived Neuroprotectin D1 Induces Neuronal Survival via Secretase- and PPARγ-Mediated Mechanisms in Alzheimer's Disease Models

Neuroprotectin D1 (NPD1) is a stereoselective mediator derived from the omega-3 essential fatty acid docosahexaenoic acid (DHA) with potent inflammatory resolving and neuroprotective bioactivity. NPD1 reduces Aβ42 peptide release from aging human brain cells and is severely depleted in Alzheimer's disease (AD) brain. Here we further characterize the mechanism of NPD1's neurogenic actions using 3xTg-AD mouse models and human neuronal-glial (HNG) cells in primary culture, either challenged with Aβ42 oligomeric peptide, or transfected with beta amyloid precursor protein (βAPP)sw (Swedish double mutation APP695sw, K595N-M596L). We also show that NPD1 downregulates Aβ42-triggered expression of the pro-inflammatory enzyme cyclooxygenase-2 (COX-2) and of B-94 (a TNF-α-inducible pro-inflammatory element) and apoptosis in HNG cells. Moreover, NPD1 suppresses Aβ42 peptide shedding by down-regulating β-secretase-1 (BACE1) while activating the α-secretase ADAM10 and up-regulating sAPPα, thus shifting the cleavage of βAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway. Use of the thiazolidinedione peroxisome proliferator-activated receptor gamma (PPARγ) agonist rosiglitazone, the irreversible PPARγ antagonist GW9662, and overexpressing PPARγ suggests that the NPD1-mediated down-regulation of BACE1 and Aβ42 peptide release is PPARγ-dependent. In conclusion, NPD1 bioactivity potently down regulates inflammatory signaling, amyloidogenic APP cleavage and apoptosis, underscoring the potential of this lipid mediator to rescue human brain cells in early stages of neurodegenerations

Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS)

Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for the identification and quantification of trace chemicals in complex mixtures. When complex samples are analyzed by GC-MS it is common to observe co-elution of two or more components, resulting in an overlap of signal peaks observed in the total ion chromatogram. In such situations manual signal analysis is often the most reliable means for the extraction of pure component signals; however, a systematic manual analysis over a number of samples is both tedious and prone to error. In the past 30 years a number of computational approaches were proposed to assist in the process of the extraction of pure signals from co-eluting GC-MS components. This includes empirical methods, comparison with library spectra, eigenvalue analysis, regression and others. However, to date no approach has been recognized as best, nor accepted as standard. This situation hampers general GC-MS capabilities, and in particular has implications for the development of robust, high-throughput GC-MS analytical protocols required in metabolic profiling and biomarker discovery. Here we first discuss the nature of GC-MS data, and then review some of the approaches proposed for the extraction of pure signals from co-eluting components. We summarize and classify different approaches to this problem, and examine why so many approaches proposed in the past have failed to live up to their full promise. Finally, we give some thoughts on the future developments in this field, and suggest that the progress in general computing capabilities attained in the past two decades has opened new horizons for tackling this important problem

Parkinson’s disease mouse models in translational research

Animal models with high predictive power are a prerequisite for translational research. The closer the similarity of a model to Parkinson’s disease (PD), the higher is the predictive value for clinical trials. An ideal PD model should present behavioral signs and pathology that resemble the human disease. The increasing understanding of PD stratification and etiology, however, complicates the choice of adequate animal models for preclinical studies. An ultimate mouse model, relevant to address all PD-related questions, is yet to be developed. However, many of the existing models are useful in answering specific questions. An appropriate model should be chosen after considering both the context of the research and the model properties. This review addresses the validity, strengths, and limitations of current PD mouse models for translational research