Individual differences in receptivity to scientific bullshit

Pseudo-profound bullshit receptivity is the tendency to perceive meaning in important-sounding, nonsense statements. To understand how bullshit receptivity differs across domains, we develop a scale to measure scientific bullshit receptivity — the tendency to perceive truthfulness in nonsensical scientific statements. Across three studies (total N = 1,948), scientific bullshit receptivity was positively correlated with pseudo-profound bullshit receptivity. Both types of bullshit receptivity were positively correlated with belief in science, conservative political beliefs, and faith in intuition. However, compared to pseudoprofound bullshit receptivity, scientific bullshit receptivity was more strongly correlated with belief in science, and less strongly correlated with conservative political beliefs and faith in intuition. Finally, scientific literacy moderated the relationship the two types of bullshit receptivity; the correlation between the two types of receptivity was weaker for individuals scoring high in scientific literacy

Individual differences in receptivity to scientific bullshit

Pseudo-profound bullshit receptivity is the tendency to perceive meaning in important-sounding, nonsense statements. To understand how bullshit receptivity differs across domains, we develop a scale to measure scientific bullshit receptivity - the tendency to perceive truthfulness in nonsensical scientific statements. Across three studies (total N = 1,948), scientific bullshit receptivity was positively correlated with pseudo-profound bullshit receptivity. Both types of bullshit receptivity were positively correlated with belief in science, conservative political beliefs, and faith in intuition. However, compared to pseudo-profound bullshit receptivity, scientific bullshit receptivity was more strongly correlated with belief in science, and less strongly correlated with conservative political beliefs and faith in intuition. Finally, scientific literacy moderated the relationship the two types of bullshit receptivity; the correlation between the two types of receptivity was weaker for individuals scoring high in scientific literacy.</p

A Variational Loop Shrinking Analogy for Handle and Tunnel Detection and {Reeb} Graph Construction on Surfaces

The humble loop shrinking property played a central role in the inception of modern topology but it has been eclipsed by more abstract algebraic formalism. This is particularly true in the context of detecting relevant non-contractible loops on surfaces where elaborate homological and/or graph theoretical constructs are favored in algorithmic solutions. In this work, we devise a variational analogy to the loop shrinking property and show that it yields a simple, intuitive, yet powerful solution allowing a streamlined treatment of the problem of handle and tunnel loop detection. Our formalization tracks the evolution of a diffusion front randomly initiated on a single location on the surface. Capitalizing on a diffuse interface representation combined with a set of rules for concurrent front interactions, we develop a dynamic data structure for tracking the evolution on the surface encoded as a sparse matrix which serves for performing both diffusion numerics and loop detection and acts as the workhorse of our fully parallel implementation. The substantiated results suggest our approach outperforms state of the art and robustly copes with highly detailed geometric models. As a byproduct, our approach can be used to construct Reeb graphs by diffusion thus avoiding commonly encountered issues when using Morse functions

Syndecan-2 is a novel target of insulin-like growth factor binding protein-3 and is over-expressed in fibrosis

Extracellular matrix deposition and tissue scarring characterize the process of fibrosis. Transforming growth factor beta (TGFβ) and Insulin-like growth factor binding protein-3 (IGFBP-3) have been implicated in the pathogenesis of fibrosis in various tissues by inducing mesenchymal cell proliferation and extracellular matrix deposition. We identified Syndecan-2 (SDC2) as a gene induced by TGFβ in an IGFBP-3-dependent manner. TGFβ induction of SDC2 mRNA and protein required IGFBP-3. IGFBP-3 independently induced production of SDC2 in primary fibroblasts. Using an ex-vivo model of human skin in organ culture expressing IGFBP-3, we demonstrate that IGFBP-3 induces SDC2 ex vivo in human tissue. We also identified Mitogen-activated protein kinase-interacting kinase (Mknk2) as a gene induced by IGFBP-3. IGFBP-3 triggered Mknk2 phosphorylation resulting in its activation. Mknk2 independently induced SDC2 in human skin. Since IGFBP-3 is over-expressed in fibrotic tissues, we examined SDC2 levels in skin and lung tissues of patients with systemic sclerosis (SSc) and lung tissues of patients with idiopathic pulmonary fibrosis (IPF). SDC2 levels were increased in fibrotic dermal and lung tissues of patients with SSc and in lung tissues of patients with IPF. This is the first report describing elevated levels of SDC2 in fibrosis. Increased SDC2 expression is due, at least in part, to the activity of two pro-fibrotic factors, TGFβ and IGFBP-3. © 2012 Ruiz et al

DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin

We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the behaviour of a numerical surface ocean wave model (Simulating WAves Nearshore, SWAN) at a sparse set of locations, thus enabling numerically cheap large-ensemble prediction over synoptic to climate timescales. DELWAVE was trained on COSMO-CLM (Climate Limited-area Model) and SWAN input data during the period of 1971–1998, tested during 1998–2000, and cross-evaluated over the far-future climate time window of 2071–2100. It is constructed from a convolutional atmospheric encoder block, followed by a temporal collapse block and, finally, a regression block. DELWAVE reproduces SWAN model significant wave heights with a mean absolute error (MAE) of between 5 and 10 cm, mean wave directions with a MAE of 10–25°, and a mean wave period with a MAE of 0.2 s. DELWAVE is able to accurately emulate multi-modal mean wave direction distributions related to dominant wind regimes in the basin. We use wave power analysis from linearised wave theory to explain prediction errors in the long-period limit during southeasterly conditions. We present a storm analysis of DELWAVE, employing threshold-based metrics of precision and recall to show that DELWAVE reaches a very high score (both metrics over 95 %) of storm detection. SWAN and DELWAVE time series are compared to each other in the end-of-century scenario (2071–2100) and compared to the control conditions in the 1971–2000 period. Good agreement between DELWAVE and SWAN is found when considering climatological statistics, with a small (≤ 5 %), though systematic, underestimate of 99th-percentile values. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal.</p

The SandS Ecosystem, a True Instance of WEB4.0

We introduce a peculiar ecosystem aimed at ruling in remote the household appliances of the members of a special social network. The keen feature of the social network is a networked intelligence, equipped with cognitive tools that enable it to provide services fully compliant with the members\u2019 needs. The scheme is the following: The appliances are internet-connected through the home Wi-Fi router. The user asks the social network for a task to be executed by his appliance (for instance, washing three kilos of woollen coloured laundry), the network, in the role of an electronic super-mom, sends directly to the washing machine an optimal sequence of commands the recipes (such as: warm the water at 34\ub0, soak for 57 minutes, etc.) to execute the task in a way that matches the user preferences, possibly green goals included. Feedbacks are sent by user and appliances themselves to the network intelligence to close the permanent recipe optimization loop, with offline advice on the part of appliance manufacturers. A properly devised user interface allows a friendly and accurate management of all interactions between the user and the social network, constituting the user-centric support of the cognitive driven services representing a genuine instance of WEB 4.0

A36 Prevalence of HIV-1 subtypes in Slovenia with an emphasis on molecular and phylogenetic investigation of subtype A

21st International BioInformatics Workshop on Virus Evolution and Molecular EpidemiologyIn Slovenia, a small country in Central Europe, less than 1 per 1,000 inhabitants are estimated to be infected with HIV-1. As in most of the Central and Western European countries, the majority of patients diagnosed with HIV-1 are infected with subtype B. However, due to migration, other subtypes can become more prevalent in the country. The aim of this study was to determine HIV-1 subtypes circulating in Slovenia and to further examine the molecular epidemiology of subtype A. A total of 367 Slovenian HIV-1 sequences were included in the study, representing 58% of all patients diagnosed in Slovenia until the end of the year 2013. Subtype was assigned by employing different HIV subtyping tools coupled with Maximum likelihood phylogenetic analysis. The latter was performed to examine the molecular epidemiology of subtype A as well. Identified clusters of Slovenian subtype A sequences were further analyzed for the determination of the time of the most recent common ancestor (tMRCA) by using Monte Carlo Markov chain (MCMC) method available in BEAST 2.1.3 software. We determined the prevalence of subtype B to be 85.3%, while subtype A was the most prevalent non-B subtype found in 18 patients (4.9%), followed by CRF02_AG (2.4%), subtype C (1.1%), subtypes D, G and CRF01_AE (0.8% each) and subtypes F1 and CRF22_01A1 (0.3% each). Subtypes could not be assigned to 12 sequences (3.3%). The phylogenetic tree obtained by ML analysis of the subtype A and subtype A related recombinants revealed that Slovenian sequences were part of 6 major international clusters. Two clusters consisting only of Slovenian sequences were identified and thus additional MCMC analysis was employed. Results of a Slovenian cluster of 4 subtype A sequences showed a posterior probability value of 1 and a tMRCA between the years 1985 and 2008, with a mean in the year 2001. In conclusion, in a Central European country, where subtype B predominates, the second most common subtype was found to be subtype A. Non-B subtypes were observed in one out of seven patients in Slovenia, a fraction that is not negligible, thus proving importance of surveillance of HIV subtype diversity and corresponding molecular epidemiology of non-B subtypes.publishersversionpublishe