A comprehensive review of the genetics of juvenile idiopathic arthritis

Juvenile idiopathic arthritis (JIA) is the most common chronic arthropathy of childhood which is believed to be influenced by both genetic and environmental factors. The progress in identifying genes underlying JIA susceptibility using candidate gene association studies has been slow. Several associations between JIA and variants in the genes encoding the human leukocyte antigens (HLA) have been confirmed and replicated in independent cohorts. However it is clear that genetic variants outside the HLA also influence susceptibility to JIA. While a large number of non-HLA candidate genes have been tested for associations, only a handful of reported associations such as PTPN22 have been validated. In this review we discuss the principles behind genetic studies of complex traits like JIA, and comprehensively catalogue non-HLA candidate-gene association studies performed in JIA to date and review several validated associations. Most candidate gene studies are underpowered and do not detect associations, and those that do are often not replicated. We also discuss the principles behind genome-wide association studies and discuss possible implications for identifying genes underlying JIA. Finally we discuss several genetic variants underlying multiple clinically distinct autoimmune phenotypes

Conjunctive explanations: when are two explanations better than one?

When is it explanatorily better to adopt a conjunction of explanatory hypotheses as opposed to committing to only some of them? Although conjunctive explanations are inevitably less probable than less committed alternatives, we argue that the answer is not ‘never’. This paper provides an account of the conditions under which explanatory considerations warrant a preference for less probable, conjunctive explanations. After setting out four formal conditions that must be met by such an account, we consider the shortcomings of several approaches. We develop an account that avoids these shortcomings and then defend it by applying it to a well-known example of explanatory reasoning in contemporary science

Validation of machine learning models to detect amyloid pathologies across institutions.

Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alzheimer's disease (AD) neuropathology practice. Computational approaches based on machine learning have recently generated quantitative scores for whole slide images (WSIs) that are highly correlated with human derived semi-quantitative scores, such as those of CERAD, for Alzheimer's disease pathology. However, the robustness of such models have yet to be tested in different cohorts. To validate previously published machine learning algorithms using convolutional neural networks (CNNs) and determine if pathological heterogeneity may alter algorithm derived measures, 40 cases from the Goizueta Emory Alzheimer's Disease Center brain bank displaying an array of pathological diagnoses (including AD with and without Lewy body disease (LBD), and / or TDP-43-positive inclusions) and levels of Aβ pathologies were evaluated. Furthermore, to provide deeper phenotyping, amyloid burden in gray matter vs whole tissue were compared, and quantitative CNN scores for both correlated significantly to CERAD-like scores. Quantitative scores also show clear stratification based on AD pathologies with or without additional diagnoses (including LBD and TDP-43 inclusions) vs cases with no significant neurodegeneration (control cases) as well as NIA Reagan scoring criteria. Specifically, the concomitant diagnosis group of AD + TDP-43 showed significantly greater CNN-score for cored plaques than the AD group. Finally, we report that whole tissue computational scores correlate better with CERAD-like categories than focusing on computational scores from a field of view with densest pathology, which is the standard of practice in neuropathological assessment per CERAD guidelines. Together these findings validate and expand CNN models to be robust to cohort variations and provide additional proof-of-concept for future studies to incorporate machine learning algorithms into neuropathological practice

Evolution and Diversity of a Fungal Self/Nonself Recognition Locus

Self/nonself discrimination is an essential feature for pathogen recognition and graft rejection and is a ubiquitous phenomenon in many organisms. Filamentous fungi, such as Neurospora crassa, provide a model for analyses of population genetics/evolution of self/nonself recognition loci due to their haploid nature, small genomes and excellent genetic/genomic resources. In N. crassa, nonself discrimination during vegetative growth is determined by 11 heterokaryon incompatibility (het) loci. Cell fusion between strains that differ in allelic specificity at any of these het loci triggers a rapid programmed cell death response.In this study, we evaluated the evolution, population genetics and selective mechanisms operating at a nonself recognition complex consisting of two closely linked loci, het-c (NCU03493) and pin-c (NCU03494). The genomic position of pin-c next to het-c is unique to Neurospora/Sordaria species, and originated by gene duplication after divergence from other species within the Sordariaceae. The het-c pin-c alleles in N. crassa are in severe linkage disequilibrium and consist of three haplotypes, het-c1/pin-c1, het-c2/pin-c2 and het-c3/pin-c3, which are equally frequent in population samples and exhibit trans-species polymorphisms. The absence of recombinant haplotypes is correlated with divergence of the het-c/pin-c intergenic sequence. Tests for positive and balancing selection at het-c and pin-c support the conclusion that both of these loci are under non-neutral balancing selection; other regions of both genes appear to be under positive selection. Our data show that the het-c2/pin-c2 haplotype emerged by a recombination event between the het-c1/pin-c1 and het-c3/pin-c3 approximately 3-12 million years ago.These results support models by which loci that confer nonself discrimination form by the association of polymorphic genes with genes containing HET domains. Distinct allele classes can emerge by recombination and positive selection and are subsequently maintained by balancing selection and divergence of intergenic sequence resulting in recombination blocks between haplotypes

Conjunctive Explanations: When Are Two Explanations Better than One?

When is it explanatorily better to adopt a conjunction of explanatory hypotheses as opposed to committing to only some of them? Although conjunctive explanations are inevitably less probable than less committed alternatives, we argue that the answer is not ‘never’. This paper provides an account of the conditions under which explanatory considerations warrant a preference for less probable, conjunctive explanations. After setting out four formal conditions that must be met by such an account, we consider the shortcomings of several approaches. We develop an account that avoids these shortcomings and then defend it by applying it to a well-known example of explanatory reasoning in contemporary science

Honeycomb Core Permeability Under Mechanical Loads

A method for characterizing the air permeability of sandwich core materials as a function of applied shear stress was developed. The core material for the test specimens was either Hexcel HRP-3/16-8.0 and or DuPont Korex-1/8-4.5 and was nominally one-half inch thick and six inches square. The facesheets where made of Hercules' AS4/8552 graphite/epoxy (Gr/Ep) composites and were nominally 0.059-in. thick. Cytec's Metalbond 1515-3M epoxy film adhesive was used for co-curing the facesheets to the core. The permeability of the specimens during both static (tension) and dynamic (reversed and non-reversed) shear loads were measured. The permeability was measured as the rate of air flow through the core from a circular 1-in2 area of the core exposed to an air pressure of 10.0 psig. In both the static and dynamic testing, the Korex core experienced sudden increases in core permeability corresponding to a core catastrophic failure, while the URP core experienced a gradual increase in the permeability prior to core failure. The Korex core failed at lower loads than the HRP core both in the transverse and ribbon directions

Neurospora crassa transcriptomics reveals oxidative stress and plasma membrane homeostasis biology genes as key targets in response to chitosan

Chitosan is a natural polymer with antimicrobial activity. Chitosan causes plasma membrane permeabilization and induction of intracellular reactive oxygen species (ROS) in Neurospora crassa. We have determined the transcriptional profile of N. crassa to chitosan and identified the main gene targets involved in the cellular response to this compound. Global network analyses showed membrane, transport and oxidoreductase activity as key nodes affected by chitosan. Activation of oxidative metabolism indicates the importance of ROS and cell energy together with plasma membrane homeostasis in N. crassa response to chitosan. Deletion strain analysis of chitosan susceptibility pointed NCU03639 encoding a class 3 lipase, involved in plasma membrane repair by lipid replacement, and NCU04537 a MFS monosaccharide transporter related to assimilation of simple sugars, as main gene targets of chitosan. NCU10521, a glutathione S-transferase-4 involved in the generation of reducing power for scavenging intracellular ROS is also a determinant chitosan gene target. Ca2+ increased tolerance to chitosan in N. crassa. Growth of NCU10610 (fig 1 domain) and SYT1 (a synaptotagmin) deletion strains was significantly increased by Ca2+ in the presence of chitosan. Both genes play a determinant role in N. crassa membrane homeostasis. Our results are of paramount importance for developing chitosan as an antifungal.This work was supported by the National Institutes of Health (USA) grant GM060468 to NLG and Spanish Ministry of Economy and Competitiveness Grant AGL 2011-29297/AGR to LVLL