The toxicological paradigms of aluminum and the etiology of Alzheimer's disease in the nematode Caenorhabditis elegans

This study examined behavioral and enzymatic changes of C. elegans from its exposure to aluminum, and the resulting relationship with Alzheimer's disease. After chronic and acute exposure to aluminum, the results indicated that it alters the cholinergic status and behavior parameters of the nematode, suggesting a relationship between exposure to aluminum and the etiology of AD.     

Social identity and applicant attraction: Exploring the role of multiple levels of self

Applicant attraction is a critical objective of recruitment. Common predictor variables of applicant attraction are limited in that they do not provide a comprehensive understanding of the process that shapes the perceptions and beliefs of job applicants about the firms for which they aspire to work for. Because individuals have the inherent desire to expand and enhance their social identities (e.g., personal, relational, and collective identities), they are likely to be attracted to organizations that allow them to do so. Building on recent work on levels of self, our paper suggests that social identities mediate the relation between currently established predictor variables of applicant attraction (e.g., compensation, type of work, and organizational image) and important applicant attraction outcomes. Common predictor variables of applicant attraction can lead to the activation, evaluation, and identification processes described by social identity theory. A theoretical framework is presented that illustrates the mediating influence of social identity on the relations between common predictor variables and applicant attraction outcomes. This framework may lead to more effective recruitment strategies. Copyright © 2015 John Wiley & Sons, Ltd.     

Spatial variance and assessment of nitrogen dioxide pollution in major cities of Pakistan along N5-Highway

This paper discusses the findings of the first car MAX-DOAS (multi-axis differential optical absorption spectroscopy) field campaign (300 km long) along the National Highway-05 (N5-Highway) of Pakistan conducted on 13 and 14 November, 2012. The main objective of the field campaign was to assess the spatial distribution of tropospheric nitrogen dioxide (NO₂) columns and corresponding concentrations along the N5-Highway from Islamabad to Lahore. Source identification of NO₂ revealed that the concentrations were higher within major cities along the highway. The highest NO₂ vertical column densities (NO₂ VCDs) were found around two major cities of Rawalpindi and Lahore. This study also presents a comparison of NO₂ VCDs measured by the ozone monitoring instrument (OMI) and car MAX-DOAS observations. The comparison revealed similar spatial distribution of the NO₂ columns with both car MAX-DOAS and satellite observations, but the car MAX-DOAS observations show much more spatial details. Maximum NO₂ VCD retrieved from car MAX-DOAS observations was up to an order of magnitude larger than the OMI observations in urban areas.     

Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning

● A novel framework integrating quantile regression with machine learning is proposed. ● It aims to identify factors driving observations to upper boundary of relationship. ● Increasing N:P and TN concentration help fulfill the effect of TP on CHL. ● Wetter and warmer decrease potential and increase eutrophication control difficulty. ● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.