Personality Traits in Miners with Past Occupational Elemental Mercury Exposure

In this study, we evaluated the impact of long-term occupational exposure to elemental mercury vapor (Hg(0)) on the personality traits of ex-mercury miners. Study groups included 53 ex-miners previously exposed to Hg(0) and 53 age-matched controls. Miners and controls completed the self-reporting Eysenck Personality Questionnaire and the Emotional States Questionnaire. The relationship between the indices of past occupational exposure and the observed personality traits was evaluated using Pearson’s correlation coefficient and on a subgroup level by machine learning methods (regression trees). The ex-mercury miners were intermittently exposed to Hg(0) for a period of 7–31 years. The means of exposure-cycle urine mercury (U-Hg) concentrations ranged from 20 to 120 μg/L. The results obtained indicate that ex-miners tend to be more introverted and sincere, more depressive, more rigid in expressing their emotions and are likely to have more negative self-concepts than controls, but no correlations were found with the indices of past occupational exposure. Despite certain limitations, results obtained by the regression tree suggest that higher alcohol consumption per se and long-term intermittent, moderate exposure to Hg(0) (exposure cycle mean U-Hg concentrations > 38.7 < 53.5 μg/L) in interaction with alcohol remain a plausible explanation for the depression associated with negative self-concept found in subgroups of ex-mercury miners. This could be one of the reason for the higher risk of suicide among miners of the Idrija Mercury Mine in the last 45 years

Emotions and personality traits in former mercury miners

The aim of this study is to evaluate the impact of long-term occupational exposure to elemental mercury vapor (Hg&deg;) on the personality traits of ex-mercury miners. The study groups included 53 ex-mercury miners previously exposed to Hg&deg; and 53 age-matched controls. Their previous occupational exposure, as well as some biological indices of actual non-occupational exposure, were evaluated. Miners and controls completed the self-reporting Eysenck Personality Questionnaire (EPQ) and the Emotional States Questionnaire (ESQ). Group differences were analyzed through the application of ANOVA software. The relationship between the indices of previous occupational exposure and the observed personality traits was evaluated by machine learning methods (regression trees). The mercury miners were intermittently exposed to Hg&deg; in intervals &#8211; cycles for a period of 7-31 years at air Hg&deg; concentrations ranging from 0.14 to 0.45 mg&#47;m3. The miners&#39; mean cycle urine mercury (U-Hg) level (range 20&#8211;120 &mu;g&#47;L) and cumulative U-Hg level (range 1286&#8211;21390 &mu;g&#47;L) were very high. The present non-occupational exposure to mercury was very low in both groups. The low extraversion and lie scores shown by EPQ suggest that miners are more introverted and sincere. The results obtained from ESQ indicate that mercury miners tend to be more depressive, more rigid in expressing their emotions (indifference), and are likely to have more negative self-concepts than the controls. The tendency towards emotional rigidity, negative self-concept, and partly also introversion seems to be associated with some biological indices of occupational Hg&deg; exposure, but not the lower score of lie found in miners. Higher occupational Hg&deg; exposure (cycles U-Hg level &gt; 38.7 mg&#47;L) in interaction with moderate alcohol consumption (&lt;26 ml&#47;day) seems to have had a decisive influence on the development of miners&#39; depression. Despite the limitations, long-term intermittent, substantial exposure to Hg&deg; in interaction with alcohol remains a plausible explanation for depression, disposition to emotional rigidity, and negative self-concept found in mercury miners in the period after exposure

Model tree (with two linear regression models, LM1 and LM2) constructed by M5′, describing the negative self-concept score and its correlation coefficient

<p><b>Copyright information:</b></p><p>Taken from "Personality Traits in Miners with Past Occupational Elemental Mercury Exposure"</p><p>Environmental Health Perspectives 2006;114(2):290-296.</p><p>Published online 18 Jan 2006</p><p>PMCID:PMC1367847.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.</p> The number of subjects in each leaf is given in parentheses. The numbers in brackets correspond to the minimal, maximal, average, median values, and (in bold if present) the relative importance factor of each numerical attribute. The relative importance factor is the product of an average value and a coefficient in the model

Multi-Directional Rule Set Learning

A rule set is a type of classifier that, given attributes X, predicts a target Y. Its main advantage over other types of classifiers is its simplicity and interpretability. A practical challenge is that the end user of a rule set does not always know in advance which target will need to be predicted. One way to deal with this is to learn a multi-directional rule set, which can predict any attribute from all others. An individual rule in such a multi-directional rule set can have multiple targets in its head, and thus be used to predict any one of these. Compared to the naive approach of learning one rule set for each possible target and merging them, a multi-directional rule set containing multi-target rules is potentially smaller and more interpretable. Training a multi-directional rule set involves two key steps: generating candidate rules and selecting rules. However, the best way to tackle these steps remains an open question. In this paper, we investigate the effect of using Random Forests as candidate rule generators and propose two new approaches for selecting rules with multi-target heads: MIDS, a generalization of the recent single-target IDS approach, and RR, a new simple algorithm focusing only on predictive performance. Our experiments indicate that (1) using multi-target rules leads to smaller rule sets with a similar predictive performance, (2) using Forest-derived rules instead of association rules leads to rule sets of similar quality, and (3) RR outperforms MIDS, underlining the usefulness of simple selection objectives.status: Published onlin

Model tree (with four linear regression models, LM1 to LM4) constructed by M5′, describing the depression score and its correlation coefficient

<p><b>Copyright information:</b></p><p>Taken from "Personality Traits in Miners with Past Occupational Elemental Mercury Exposure"</p><p>Environmental Health Perspectives 2006;114(2):290-296.</p><p>Published online 18 Jan 2006</p><p>PMCID:PMC1367847.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.</p> The number of subjects in each leaf is given in parentheses. The numbers in brackets correspond to the minimal, maximal, average, median values, and (in bold if present) the relative importance factor of each numerical attribute. Relative importance factor is the product of an average value and a coefficient in the model

How to carve up the world: Learning and collaboration for structure recommendation

Structuring is one of the fundamental activities needed to understand data. Human structuring activity lies behind many of the datasets found on the internet that contain grouped instances, such as file or email folders, tags and bookmarks, ontologies and linked data. Understanding the dynamics of large-scale structuring activities is a key prerequisite for theories of individual behaviour in collaborative settings as well as for applications such as recommender systems. One central question is to what extent the "structurer" - be it human or machine - is driven by his/its own prior structures, and to what extent by the structures created by others such as one's communities. In this paper, we propose a method for identifying these dynamics. The method relies on dynamic conceptual clustering, and it simulates an intellectual structuring process operating over an extended period of time. The development of a grouping of dynamically changing items follows a dynamically changing and collectively determined "guiding grouping". The analysis of a real-life dataset of a platform for literature management suggests that even in such a typical "Web 2.0" environment, users are guided somewhat more by their own previous behaviour than by their peers. Furthermore, we also illustrate how the presented method can be used to recommend structure to the user. © Springer-Verlag 2013.status: publishe