Not-So-Negative Selection

It once seemed clear that negative selection of self-specific T cells in the thymus was the major mechanism of central tolerance. But recent studies, including Legoux et al. (2015) in this issue of Immunity, show that this is not always the case

A conditional marker gene allowing both positive and negative selection in plants

Selectable markers enable transgenic plants or cells to be identified after transformation. They can be divided into positive and negative markers conferring a selective advantage or disadvantage, respectively. We present a marker gene, dao1, encoding D-amino acid oxidase (DAAO, EC 1.4.3.3) that can be used for either positive or negative selection, depending on the substrate. DAAO catalyzes the oxidative deamination of a range of D-amino acids. Selection is based on differences in the toxicity of different D-amino acids and their metabolites to plants. Thus, D-alanine and D-serine are toxic to plants, but are metabolized by DAAO into nontoxic products, whereas D-isoleucine and D-valine have low toxicity, but are metabolized by DAAO into the toxic keto acids 3-methyl-2-oxopentanoate and 3-methyl-2-oxobutanoate, respectively. Hence, both positive and negative selection is possible with the same marker gene. The marker has been successfully established in Arabidopsis thaliana, and proven to be versatile, rapidly yielding unambiguous results, and allowing selection immediately after germination

Identification of MHC Class II Binders/ Non-binders using Negative Selection Algorithm

The identification of major histocompatibility complex (MHC) class-II restricted peptides is an important goal in human immunological research leading to peptide based vaccine design. These MHC class–II peptides are predominantly recognized by CD4+ T-helper cells, which when turned on, have profound immune regulatory effects. Thus, prediction of such MHC class-II binding peptides is very helpful towards epitope-based vaccine design. HLA-DR proteins were found to be associated with autoimmune diseases e.g. HLA-DRB1*0401 with rheumatoid arthritis. It is important for the treatment of autoimmune diseases to determine which peptides bind to MHC class II molecules. The experimental methods for identification of these peptides are both time consuming and cost intensive. Therefore, computational methods have been found helpful in classifying these peptides as binders or non-binders. We have applied negative selection algorithm, an artificial immune system approach to predict MHC class–II binders and non-binders. For the evaluation of the NSA algorithm, five fold cross validation has been used and six MHC class–II alleles have been taken. The average area under ROC curve for HLA-DRB1*0301, DRB1*0401, DRB1*0701, DRB1*1101, DRB1*1501, DRB1*1301 have been found to be 0.75, 0.77, 0.71, 0.72, and 0.69, and 0.84 respectively indicating good predictive performance for the small training set

Real valued negative selection for anomaly detection in wireless ad hoc networks

Wireless ad hoc network is one of the network technologies that have gained lots of attention from computer scientists for the future telecommunication applications. However it has inherits the major vulnerabilities from its ancestor (i.e., the fixed wired networks) but cannot inherit all the conventional intrusion detection capabilities due to its features and characteristics. Wireless ad hoc network has the potential to become the de facto standard for future wireless networking because of its open medium and dynamic features. Non-infrastructure network such as wireless ad hoc networks are expected to become an important part of 4G architecture in the future. In this paper, we study the use of an Artificial Immune System (AIS) as anomaly detector in a wireless ad hoc network. The main goal of our research is to build a system that can learn and detect new and unknown attacks. To achieve our goal, we studied how the real-valued negative selection algorithm can be applied in wireless ad hoc network network and finally we proposed the enhancements to real-valued negative selection algorithm for anomaly detection in wireless ad hoc network

Negative Selection in Social Insects

Eusociality, characterized by cooperative brood care, and reproductive division of labor, evolved independently in insects. The evolution of eusociality has been hypothesized to lead to differences in the extent of both positive and negative selection. While population genomics studies of eusocial insects have so far focused on positive selection, there have been fewer studies on negative selection in social insects, and its relationship to the evolution of caste-biased genes. To address this knowledge gap, our research estimated the extent of negative selection in honey bees, bumblebees, and paper wasps. We show a significant negative correlation between increasing social complexity and negative selection, suggesting effective population size affects the strength of negative selection. We identified a significantly stronger negative selection in queen traits relative to worker traits in honey bees. Lastly, we observe stronger negative selection in drone traits relative to queen traits in honeybees, possible due to the haplodiploidy system

The Importance of Self-Selection in Casino Cannibalization of State Lotteries

This note extends the work of Elliott and Navin (2002) on the substitutability of commercial casinos and state lotteries by controlling for a potential negative selection bias. We utilize a Heckman two-step selection correction in which our first stage probit involves whether or not a state has legalized commercial casinos. Results indicate that a $1 increase in state casino tax revenue will reduce net lottery proceeds by$0.56. This estimate is 33% smaller than what has been found in other studies, which is consistent with a negative selection bias.

International Migration in the Long-Run: Positive Selection, Negative Selection and Policy

Most labor scarce overseas countries moved decisively to restrict their immigration during the first third of the 20th century. This autarchic retreat from unrestricted and even publicly-subsidized immigration in the first global century before World War I to the quotas and bans introduced afterwards was the result of a combination of factors: public hostility towards new immigrants of lower quality public assessment of the impact of those immigrants on a deteriorating labor market, political participation of those impacted, and, as a triggering mechanism, the sudden shocks to the labor market delivered by the 1890s depression, the Great War, postwar adjustment and the great depression. The paper documents the secular drift from very positive to much more negative immigrant selection which took place in the first global century after 1820 and in the second global century after 1950, and seeks explanations for it. It then explores the political economy of immigrant restriction in the past and seeks historical lessons for the present.