On the inclusion ideal graph of a poset

Let (P,≤) be an atomic partially ordered set (poset, briefly) with a minimum element 0 and \u1d57f(P) the set of nontrivial ideals of P. The inclusion ideal graph of P, denoted by Ω(P), is an undirected and simple graph with the vertex set \u1d57f(P) and two distinct vertices I, J ∈ \u1d57f(P) are adjacent in Ω(P) if and only if I ⊂ J or J ⊂ I. We study some connections between the graph theoretic properties of this graph and some algebraic properties of a poset. We prove that Ω(P) is not connected if and only if P = {0, a1, a2}, where a1, a2 are two atoms. Moreover, it is shown that if Ω(P) is connected, then diam(Ω(P)) ≤ 3. Also, we show that if Ω(P) contains a cycle, then girth(Ω(P)) ∈ {3, 6}. Furthermore, all posets based on their diameters and girths of inclusion ideal graphs are characterized. Among other results, all posets whose inclusion ideal graphs are path, cycle and star are characterized

Exploring Lightweight Interventions at Posting Time to Reduce the Sharing of Misinformation on Social Media

When users on social media share content without considering its veracity, they may unwittingly be spreading misinformation. In this work, we investigate the design of lightweight interventions that nudge users to assess the accuracy of information as they share it. Such assessment may deter users from posting misinformation in the first place, and their assessments may also provide useful guidance to friends aiming to assess those posts themselves. In support of lightweight assessment, we first develop a taxonomy of the reasons why people believe a news claim is or is not true; this taxonomy yields a checklist that can be used at posting time. We conduct evaluations to demonstrate that the checklist is an accurate and comprehensive encapsulation of people's free-response rationales. In a second experiment, we study the effects of three behavioral nudges -- 1) checkboxes indicating whether headings are accurate, 2) tagging reasons (from our taxonomy) that a post is accurate via a checklist and 3) providing free-text rationales for why a headline is or is not accurate -- on people's intention of sharing the headline on social media. From an experiment with 1668 participants, we find that both providing accuracy assessment and rationale reduce the sharing of false content. They also reduce the sharing of true content, but to a lesser degree that yields an overall decrease in the fraction of shared content that is false. Our findings have implications for designing social media and news sharing platforms that draw from richer signals of content credibility contributed by users. In addition, our validated taxonomy can be used by platforms and researchers as a way to gather rationales in an easier fashion than free-response.Comment: To appear in CSCW'2

Survey and classification of functional characteristics in neural network technique for the diagnosis of ischemic heart disease: A systematic review

Background: Nowadays, the prevalence of ischemic heart diseases (IHDs) leads to destructive effects such as patient death. Late diagnosis of such diseases as well as their invasive diagnostic approaches made researchers provide a decision support system based on neural network techniques, while using minimum data set for timely diagnosis. In this regard, selecting minimum useful features is significant for designing neural network structure and it paves the way to attain maximum accuracy in obtaining the results. Methods: In this systematic review, valid databases using sensitive keywords were initially searched out to find articles related to "diagnosing the ischemic heart disease using artificial neural networks" and afterwards, scientific methods were used to analyze and classify the content. Findings: Researchers applied various extractable features from demographic data, medical history, signs and symptoms, and paraclinical examinations, to design the neural network structure. Among them, the features obtained from electrocardiographic test, embedded in paraclinical examinations, had led to a remarkable increase of efficiency in neural network. Conclusion: Utilizing such diagnostic decision support systems in practical environments depends on their high confidence coefficient and physicians� acceptability. Therefore, it can be useful to improve maturity in the design of the neural network structure depending on the choice of the minimum optimal features, and to create required infrastructures to input patients� real, accurate, and flowing data in these systems. © 2018, Isfahan University of Medical Sciences(IUMS). All rights reserved

Survey and classification of functional characteristics in neural network technique for the diagnosis of ischemic heart disease: A systematic review

Background: Nowadays, the prevalence of ischemic heart diseases (IHDs) leads to destructive effects such as patient death. Late diagnosis of such diseases as well as their invasive diagnostic approaches made researchers provide a decision support system based on neural network techniques, while using minimum data set for timely diagnosis. In this regard, selecting minimum useful features is significant for designing neural network structure and it paves the way to attain maximum accuracy in obtaining the results. Methods: In this systematic review, valid databases using sensitive keywords were initially searched out to find articles related to "diagnosing the ischemic heart disease using artificial neural networks" and afterwards, scientific methods were used to analyze and classify the content. Findings: Researchers applied various extractable features from demographic data, medical history, signs and symptoms, and paraclinical examinations, to design the neural network structure. Among them, the features obtained from electrocardiographic test, embedded in paraclinical examinations, had led to a remarkable increase of efficiency in neural network. Conclusion: Utilizing such diagnostic decision support systems in practical environments depends on their high confidence coefficient and physicians� acceptability. Therefore, it can be useful to improve maturity in the design of the neural network structure depending on the choice of the minimum optimal features, and to create required infrastructures to input patients� real, accurate, and flowing data in these systems. © 2018, Isfahan University of Medical Sciences(IUMS). All rights reserved

Review of Microfluidic Devices and Imaging Techniques for Fluid Flow Study in Porous Geomaterials

Understanding transport phenomena and governing mechanisms of different physical and chemical processes in porous media has been a critical research area for decades. Correlating fluid flow behaviour at the micro-scale with macro-scale parameters, such as relative permeability and capillary pressure, is key to understanding the processes governing subsurface systems, and this in turn allows us to improve the accuracy of modelling and simulations of transport phenomena at a large scale. Over the last two decades, there have been significant developments in our understanding of pore-scale processes and modelling of complex underground systems. Microfluidic devices (micromodels) and imaging techniques, as facilitators to link experimental observations to simulation, have greatly contributed to these achievements. Although several reviews exist covering separately advances in one of these two areas, we present here a detailed review integrating recent advances and applications in both micromodels and imaging techniques. This includes a comprehensive analysis of critical aspects of fabrication techniques of micromodels, and the most recent advances such as embedding fibre optic sensors in micromodels for research applications. To complete the analysis of visualization techniques, we have thoroughly reviewed the most applicable imaging techniques in the area of geoscience and geo-energy. Moreover, the integration of microfluidic devices and imaging techniques was highlighted as appropriate. In this review, we focus particularly on four prominent yet very wide application areas, namely “fluid flow in porous media”, “flow in heterogeneous rocks and fractures”, “reactive transport, solute and colloid transport”, and finally “porous media characterization”. In summary, this review provides an in-depth analysis of micromodels and imaging techniques that can help to guide future research in the in-situ visualization of fluid flow in porous media

Study on Prevalence of TTV among Cirrhotic patients due to Hepatitis B & C in Ahwaz University Hospitals during the Years 2004-2005

Background and Aims: Recently, a novel DNA virus was isolated from the serum of a patient with post-transfusion non A-G hepatitis and named TT virus. The aim of this study was to determine the prevalence TT virus among cirrhotic patients due to hepatitis B & C in infection Ahwaz. Methods: The prevalence of TTV infection was studied in 41 patients with liver cirrhosis. TTV DNA was detected by semi-nested PCR. The plasma samples were tested for marker hepatitis B & C by ELISA test. Results: TT virus was detected in 17(41.46%) of the 41 patients with cirrhotic liver disease. There were no significant difference between the subject TTV DNA in relation to sex and age. TTV positivity in cirrhotic patient infected with hepatitis B (52.9%) was higher than in similar patients infected with hepatitis C (47.1%). Conclusion: TTV infection was highly prevalence in patient with cirrhotic hepatitis, especially in those with hepatitis B virus infection

Sept8/SEPTIN8 involvement in cellular structure and kidney damage is identified by genetic mapping and a novel human tubule hypoxic model.

Chronic kidney disease (CKD), which can ultimately progress to kidney failure, is influenced by genetics and the environment. Genes identified in human genome wide association studies (GWAS) explain only a small proportion of the heritable variation and lack functional validation, indicating the need for additional model systems. Outbred heterogeneous stock (HS) rats have been used for genetic fine-mapping of complex traits, but have not previously been used for CKD traits. We performed GWAS for urinary protein excretion (UPE) and CKD related serum biochemistries in 245 male HS rats. Quantitative trait loci (QTL) were identified using a linear mixed effect model that tested for association with imputed genotypes. Candidate genes were identified using bioinformatics tools and targeted RNAseq followed by testing in a novel in vitro model of human tubule, hypoxia-induced damage. We identified two QTL for UPE and five for serum biochemistries. Protein modeling identified a missense variant within Septin 8 (Sept8) as a candidate for UPE. Sept8/SEPTIN8 expression increased in HS rats with elevated UPE and tubulointerstitial injury and in the in vitro hypoxia model. SEPTIN8 is detected within proximal tubule cells in human kidney samples and localizes with acetyl-alpha tubulin in the culture system. After hypoxia, SEPTIN8 staining becomes diffuse and appears to relocalize with actin. These data suggest a role of SEPTIN8 in cellular organization and structure in response to environmental stress. This study demonstrates that integration of a rat genetic model with an environmentally induced tubule damage system identifies Sept8/SEPTIN8 and informs novel aspects of the complex gene by environmental interactions contributing to CKD risk