Comparison and Weighted Summation Type of Fuzzy Cluster Validity Indices

Finding the optimal cluster number and validating the partition resultsof a data set are difficult tasks since clustering is an unsupervised learning process.Cluster validity index (CVI) is a kind of criterion function for evaluating the clusteringresults and determining the optimal number of clusters. In this paper, we present anextensive comparison of ten well-known CVIs for fuzzy clustering. Then we extendtraditional single CVIs by introducing the weighted method and propose a weightedsummation type of CVI (WSCVI). Experiments on nine synthetic data sets and fourreal-world UCI data sets demonstrate that no one CVI performs better on all datasets than others. Nevertheless, the proposed WSCVI is more effective by properlysetting the weights

Quantifying the Clinical Significance of Cannabis Withdrawal

Background and Aims: Questions over the clinical significance of cannabis withdrawal have hindered its inclusion as a discrete cannabis induced psychiatric condition in the Diagnostic and Statistical Manual of Mental Disorders (DSM IV). This study aims to quantify functional impairment to normal daily activities from cannabis withdrawal, and looks at the factors predicting functional impairment. In addition the study tests the influence of functional impairment from cannabis withdrawal on cannabis use during and after an abstinence attempt. Methods and Results: A volunteer sample of 49 non-treatment seeking cannabis users who met DSM-IV criteria for dependence provided daily withdrawal-related functional impairment scores during a one-week baseline phase and two weeks of monitored abstinence from cannabis with a one month follow up. Functional impairment from withdrawal symptoms was strongly associated with symptom severity (p = 0.0001). Participants with more severe cannabis dependence before the abstinence attempt reported greater functional impairment from cannabis withdrawal (p = 0.03). Relapse to cannabis use during the abstinence period was associated with greater functional impairment from a subset of withdrawal symptoms in high dependence users. Higher levels of functional impairment during the abstinence attempt predicted higher levels of cannabis use at one month follow up (p = 0.001). Conclusions: Cannabis withdrawal is clinically significant because it is associated with functional impairment to normal daily activities, as well as relapse to cannabis use. Sample size in the relapse group was small and the use of a non-treatment seeking population requires findings to be replicated in clinical samples. Tailoring treatments to target withdrawal symptoms contributing to functional impairment during a quit attempt may improve treatment outcomes

Quantifying the Clinical Significance of Cannabis Withdrawal

Background and Aims: Questions over the clinical significance of cannabis withdrawal have hindered its inclusion as a discrete cannabis induced psychiatric condition in the Diagnostic and Statistical Manual of Mental Disorders (DSM IV). This study aims to quantify functional impairment to normal daily activities from cannabis withdrawal, and looks at the factors predicting functional impairment. In addition the study tests the influence of functional impairment from cannabis withdrawal on cannabis use during and after an abstinence attempt. Methods and Results: A volunteer sample of 49 non-treatment seeking cannabis users who met DSM-IV criteria for dependence provided daily withdrawal-related functional impairment scores during a one-week baseline phase and two weeks of monitored abstinence from cannabis with a one month follow up. Functional impairment from withdrawal symptoms was strongly associated with symptom severity (p = 0.0001). Participants with more severe cannabis dependence before the abstinence attempt reported greater functional impairment from cannabis withdrawal (p = 0.03). Relapse to cannabis use during the abstinence period was associated with greater functional impairment from a subset of withdrawal symptoms in high dependence users. Higher levels of functional impairment during the abstinence attempt predicted higher levels of cannabis use at one month follow up (p = 0.001). Conclusions: Cannabis withdrawal is clinically significant because it is associated with functional impairment to normal daily activities, as well as relapse to cannabis use. Sample size in the relapse group was small and the use of a non-treatment seeking population requires findings to be replicated in clinical samples. Tailoring treatments to target withdrawal symptoms contributing to functional impairment during a quit attempt may improve treatment outcomes. © 2012 Allsop et al

ABO genotype alters the gut microbiota by regulating GalNAc levels in pigs.

peer reviewedThe composition of the intestinal microbiome varies considerably between individuals and is correlated with health1. Understanding to what extend and how host genetics contributes to this variation is paramount yet has proven difficult as few associations have been replicated, particularly in humans2. We herein study the effect of host genotype on the composition of the intestinal microbiota in a large mosaic pig population. We show that, under conditions of exacerbated genetic diversity and environmental uniformity, microbiota composition and abundance of specific taxa are heritable. We map a quantitative trait locus affecting the abundance of Erysipelotrichaceae species and show that it is caused by a 2.3-Kb deletion in the N-acetyl-galactosaminyl-transferase gene underpinning the ABO blood group in humans. We show that this deletion is a ≥3.5 million years old trans-species polymorphism under balancing selection. We demonstrate that it decreases the concentrations of N-acetyl-galactosamine in the gut thereby reducing the abundance of Erysipelotrichaceae that can import and catabolize N-acetyl-galactosamine. Our results provide very strong evidence for an effect of host genotype on the abundance of specific bacteria in the intestine combined with insights in the molecular mechanisms that underpin this association. They pave the way towards identifying the same effect in human rural populations

An attribute weight based feedback model for multiple attributive group decision analysis problems with group consensus requirements in evidential reasoning context

In an evidential reasoning context, a group consensus (GC) based approach can model multiple attributive group decision analysis problems with GC requirements. The predefined GC is reached through several rounds of group analysis and discussion (GAD) in the approach. However, the GAD with no guidance may not be the most appropriate way to reach the predefined GC because several rounds of GAD will spend a lot of time of all experts and yet cannot help them to effectively emphasize on the assessments which primarily damage the GC. In this paper, an attribute weight based feedback model is constructed to effectively identify the assessments primarily damaging the GC and accelerate the GC convergence. Considering important attributes with the weights more than or at least equal to the mean of the weights of all attributes, the feedback model constructs identification rules to identify the assessments damaging the GC for the experts to renew. In addition, a suggestion rule is introduced to generate appropriate recommendations for the experts to renew their identified assessments. The identification rules are constructed at three levels including the attribute, alternative and global levels. The feedback model is used to solve an engineering project management software selection problem to demonstrate its detailed implementation process, its validity and applicability, and its advantages compared with the GC based approach.Decision analysis Multiple attributive group decision analysis Evidential reasoning approach Group consensus Attribute weight Feedback model

The Premium of Public Perceived Greenery: A Framework Using Multiscale GWR and Deep Learning

Population agglomeration and real estate development encroach on public green spaces, threatening human settlement equity and perceptual experience. Perceived greenery is a vital interface for residents to interact with the urban eco-environment. Nevertheless, the economic premiums and spatial scale of such greenery have not been fully studied because a comprehensive quantitative framework is difficult to obtain. Here, taking advantage of big geodata and deep learning to quantify public perceived greenery, we integrate a multiscale GWR (MGWR) and a hedonic price model (HPM) and propose an analytic framework to explore the premium of perceived greenery and its spatial pattern at the neighborhood scale. Our empirical study in Beijing demonstrated that (1) MGWR-based HPM can lead to good performance and increase understanding of the spatial premium effect of perceived greenery; (2) for every 1% increase in neighborhood-level perceived greenery, economic premiums increase by 4.1% (115,862 RMB) on average; and (3) the premium of perceived greenery is spatially imbalanced and linearly decreases with location, which is caused by Beijing’s monocentric development pattern. Our framework provides analytical tools for measuring and mapping the capitalization of perceived greenery. Furthermore, the empirical results can provide positive implications for establishing equitable housing policies and livable neighborhoods