Exploring the Use of Virtual Worlds as a Scientific Research Platform: The Meta-Institute for Computational Astrophysics (MICA)

We describe the Meta-Institute for Computational Astrophysics (MICA), the first professional scientific organization based exclusively in virtual worlds (VWs). The goals of MICA are to explore the utility of the emerging VR and VWs technologies for scientific and scholarly work in general, and to facilitate and accelerate their adoption by the scientific research community. MICA itself is an experiment in academic and scientific practices enabled by the immersive VR technologies. We describe the current and planned activities and research directions of MICA, and offer some thoughts as to what the future developments in this arena may be.Comment: 15 pages, to appear in the refereed proceedings of "Facets of Virtual Environments" (FaVE 2009), eds. F. Lehmann-Grube, J. Sablating, et al., ICST Lecture Notes Ser., Berlin: Springer Verlag (2009); version with full resolution color figures is available at http://www.mica-vw.org/wiki/index.php/Publication

Hair cortisol in the perinatal period mediates associations between maternal adversity and disrupted maternal interaction in early infancy

Existing literature points to the possibility that cortisol could be one link between maternal adversity and poorer parenting quality, but most studies have examined salivary cortisol concentrations rather than hair cortisol concentrations. The current study examined hair cortisol concentration (HCC) during the third trimester of pregnancy as a mediator between maternal adversity indicators (childhood abuse, severe mental illness, symptomatic functioning) and maternal caregiving behavior at 4 months postpartum. Forty‐four women participated in the study: 30 with severe mental disorders, and 14 nonclinical controls. HCC was assessed during the third trimester of pregnancy (HCC‐P) and at 4 months postpartum (HCC‐4M). Sexual, physical, and emotional abuse were assessed by the Adverse Childhood Experiences Study Questionnaire. Maternal disrupted interaction was reliably coded from mother–infant video interactions during a Still‐Face Procedure. Mediation models indicated that maternal HCC‐P and HCC‐4M mediated associations between maternal psychopathology (severe mental illness, symptomatic functioning) and maternal disrupted interaction at 4 months. Maternal HCC at 4 months also mediated associations between experienced childhood abuse and overall disrupted interaction. Our findings indicate that HCC may be a potential early biomarker for future caregiving challenges among mothers with severe mental illness and histories of childhood abuse

ExSS 2018: Workshop on explainable smart systems

Smart systems that apply complex reasoning to make decisions and plan behavior are often difficult for users to understand. While research to make systems more explainable and therefore more intelligible and transparent is gaining pace, there are numerous issues and problems regarding these systems that demand further attention. The goal of this workshop is to bring academia and industry together to address these issues. The workshop includes a keynote, poster panels, and group activities, towards developing concrete approaches to handling challenges related to the design, development, and evaluation of explainable smart systems

Pattern and process in Amazon tree turnover, 1976-2001

Previous work has shown that tree turnover, tree biomass and large liana densities have increased in mature tropical forest plots in the late twentieth century. These results point to a concerted shift in forest ecological processes that may already be having significant impacts on terrestrial carbon stocks, fluxes and biodiversity. However, the findings have proved controversial, partly because a rather limited number of permanent plots have been monitored for rather short periods. The aim of this paper is to characterize regional-scale patterns of 'tree turnover' (the rate with which trees die and recruit into a population) by using improved datasets now available for Amazonia that span the past 25 years. Specifically, we assess whether concerted changes in turnover are occurring, and if so whether they are general throughout the Amazon or restricted to one region or environmental zone. In addition, we ask whether they are driven by changes in recruitment, mortality or both. We find that: (i) trees 10 cm or more in diameter recruit and die twice as fast on the richer soils of southern and western Amazonia than on the poorer soils of eastern and central Amazonia; (ii) turnover rates have increased throughout Amazonia over the past two decades; (iii) mortality and recruitment rates have both increased significantly in every region and environmental zone, with the exception of mortality in eastern Amazonia; (iv) recruitment rates have consistently exceeded mortality rates; (v) absolute increases in recruitment and mortality rates are greatest in western Amazonian sites; and (vi) mortality appears to be lagging recruitment at regional scales. These spatial patterns and temporal trends are not caused by obvious artefacts in the data or the analyses. The trends cannot be directly driven by a mortality driver (such as increased drought or fragmentation-related death) because the biomass in these forests has simultaneously increased. Our findings therefore indicate that long-acting and widespread environmental changes are stimulating the growth and productivity of Amazon forests

Exploiting Visual Languages Generation and UML Meta Modeling to Construct Meta-CASE Workbenches

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Induction of Apoptosis Coupled to Endoplasmic Reticulum Stress in Human Prostate Cancer Cells by n-butylidenephthalide

BACKGROUND: N-butylidenephthalide (BP) exhibits antitumor effect in a variety of cancer cell lines. The objective of this study was to obtain additional insights into the mechanisms involved in BP induced cell death in human prostate cancer cells. METHODS/PRINCIPAL FINDINGS: Two human prostate cancer cell lines, PC-3 and LNCaP, were treated with BP, and subsequently evaluated for their viability and cell cycle profiles. BP caused cell cycle arrest and cell death in both cell lines. The G0/G1 phase arrest was correlated with increase levels of CDK inhibitors (p16, p21 and p27) and decrease of the checkpoint proteins. To determine the mechanisms of BP-induced growth arrest and cell death in prostate cancer cell lines, we performed a microarray study to identify alterations in gene expression induced by BP in the LNCaP cells. Several BP-induced genes, including the GADD153/CHOP, an endoplasmic reticulum stress (ER stress)-regulated gene, were identified. BP-induced ER stress was evidenced by increased expression of the downstream molecules GRP78/BiP, IRE1-α and GADD153/CHOP in both cell lines. Blockage of IRE1-α or GADD153/CHOP expression by siRNA significantly reduced BP-induced cell death in LNCaP cells. Furthermore, blockage of JNK1/2 signaling by JNK siRNA resulted in decreased expression of IRE1-α and GADD153/CHOP genes, implicating that BP-induced ER stress may be elicited via JNK1/2 signaling in prostate cancer cells. BP also suppressed LNCaP xenograft tumor growth in NOD-SCID mice. It caused 68% reduction in tumor volume after 18 days of treatment. CONCLUSIONS: Our results suggest that BP can cause G0/G1 phase arrest in prostate cancer cells and its cytotoxicity is mediated by ER stress induction. Thus, BP may serve as an anticancer agent by inducing ER stress in prostate cancer

Integrating Multiple Sketch Recognition Methods to Improve Accuracy and Speed

Sketch recognition is the computer understanding of hand drawn diagrams. Recognizing sketches instantaneously is necessary to build beautiful interfaces with real time feedback. There are various techniques to quickly recognize sketches into ten or twenty classes. However for much larger datasets of sketches from a large number of classes, these existing techniques can take an extended period of time to accurately classify an incoming sketch and require significant computational overhead. Thus, to make classification of large datasets feasible, we propose using multiple stages of recognition. In the initial stage, gesture-based feature values are calculated and the trained model is used to classify the incoming sketch. Sketches with an accuracy less than a threshold value, go through a second stage of geometric recognition techniques. In the second geometric stage, the sketch is segmented, and sent to shape-specific recognizers. The sketches are matched against predefined shape descriptions, and confidence values are calculated. The system outputs a list of classes that the sketch could be classified as, along with the accuracy, and precision for each sketch. This process both significantly reduces the time taken to classify such huge datasets of sketches, and increases both the accuracy and precision of the recognition