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OBJECTIVE To identify serum protein biomarkers that might distinguish patients with early inflammatory arthritis (IA) with psoriatic arthritis (PsA) from those with rheumatoid arthritis (RA) and may be used to support appropriate early intervention. METHODS The serum proteome of patients with PsA and patients with RA was interrogated using nano-liquid chromatography mass spectrometry (nano-LC-MS/MS) (n = 64 patients), an aptamer-based assay (SomaScan) targeting 1,129 proteins (n = 36 patients), and a multiplexed antibody assay (Luminex) for 48 proteins (n = 64 patients). Multiple reaction monitoring (MRM) assays were developed to evaluate the performance of putative markers using the discovery cohort (n = 60 patients) and subsequently an independent cohort of PsA and RA patients (n = 167). RESULTS Multivariate machine learning analysis of the protein discovery data from the 3 platforms revealed that it was possible to differentiate PsA patients from RA patients with an area under the curve (AUC) of 0.94 for nano-LC-MS/MS, 0.69 for bead-based immunoassay measurements, and 0.73 for aptamer-based analysis. Subsequently, in the separate verification and evaluation studies, random forest models revealed that a subset of proteins measured by MRM could differentiate PsA and RA patients with AUCs of 0.79 and 0.85, respectively. CONCLUSION We present a serum protein biomarker panel that can separate patients with early-onset IA with PsA from those with RA. With continued evaluation and refinement using additional and larger patient cohorts, including those with other arthropathies, we suggest that the panel identified here could contribute to improved clinical decision making.

Earthquake hazard assessments for the Tokyo Region are complicated by the trench–trench triple junction where the oceanic Philippine Sea Plate not only underthrusts a continental plate but is also being subducted by the Pacific Plate. Great thrust earthquakes and associated tsunamis are historically recognized hazards from the Continental/Philippine Sea (Sagami Trough) and Continental/Pacific (Japan Trench) plate boundaries but not from the Philippine Sea/Pacific (Izu–Bonin Trench) boundary alone. Here we employed a series of historical and hypothetical rupture models to explain the widespread distribution of geological evidence for an unusually large tsunami found along 50 km of coastline east of Tokyo. Dating to about 1,000 years ago, this inferred tsunami predates local written history by several hundred years. We found that the inland extent of its sand sheet is best explained, in computer simulations, by displacement on one of the three plate boundaries offshore of the Boso Peninsula, which corresponds to the triple junction. The minimum magnitude scenario capable of generating the inland extent of inundation involves displacement along the Philippine Sea/Pacific boundary megathrust. This plate-boundary fault adds another potential source for earthquakes in the Tokyo Region and tsunamis in the Pacific Ocean. The Philippine Sea/Pacific boundary megathrust is another possible source of seismic hazard in the Tokyo Region and tsunamis in the Pacific, according to an assessment of 1,000 years of tsunami deposits along the Japanese coastline.

Additive manufacturing processes, such as Laser Powder Bed Fusion (L-PBF), facilitates the manufacture of porous biomaterials structures, which can be used for example to enhance bone tissue regeneration. In-situ process monitoring techniques such as meltpool emission monitoring are increasingly being applied for the monitoring of the l -PBF processes. This paper investigates the use of statistical anomaly detection to analyse in-situ process monitoring data obtained during l -PBF. In this study a Renishaw 500M was used to produce porous structures, using Ti-6Al-4 V feedstock powder. During the l -PBF process, a co-axial photodiode-based process monitoring system was utilised to generate data relating to both the meltpool and the operational behaviour of the laser. Porous structures were created with intentionally defective layers, whereby the laser power was selectively reduced at specific layers. Control samples were also created where no intentionally defective layers were created. In addition, an un-intentionally defective sample was also analysed. The Generalized Extreme Studentized Deviate (GESD) test was employed to identify any defective layers within the structures. When this approach was applied to data generated during the processing of the structures with reduced input energy layers, the number of defective layers identified corresponded exactly with the known amount. When the test was run on the meltpool data, corresponding to the un-intentional defective structure, 30 layers were identified as defective. When examined, the identified layers corresponded to the physical location of the defect within the sample. The results obtained in this study indicate that the GESD test is an effective and computationally inexpensive method of identifying defective layers created during the l -PBF process.

The ability to generate rotation in ellipsoidal catalytic micro swimming devices by catalyst deposition at glancing angles allows reliable access to circling trajectories. This behaviour could enable propulsive ellipsoids to gather cargo and enhance mixing at small scales. Catalytically propelled colloidal rotation has been previously achieved in spherical swimming devices by means of neighbour shadowing during catalyst deposition leading to non-symmetrical coatings. However in this work shadowing effects arise due to the ellipsoid's inherent anisotropy, removing the requirement for a closely packed colloidal crystal monolayer. We use geometric analysis of the catalyst deposition process, and mechanistic understanding to propose a link between the observed trajectories and the catalyst distribution and suggest further routes to improve control of rotation rates.

This study investigates the use of a statistical anomaly detection method to analyse in-situ process monitoring data obtained during the Laser-Powder Bed Fusion of Ti-6Al-4V parts. The printing study was carried out on a Renishaw 500M Laser-Powder Bed Fusion system. A photodiode-based system called InfiniAM was used to monitor the melt-pool emissions along with the operational behaviour of the laser during the build process. The analysis of the in-process data was carried out using an unsupervised machine learning approach called the Search and TRace AnomalY algorithm. The ability to detect defects during the manufacturing of metal alloy parts was demonstrated.

We provide an updated sea level dataset for Dublin for the period 1938–2016 at yearly resolution. Using a newly collated sea level record for Dublin Port, as well as two nearby tide gauges at Arklow and Howth Harbour, we perform data quality checks and calibration of the Dublin Port record by adjusting the biased high water level measurements that affect the overall calculation of mean sea level (MSL). To correct these MSL values, we use a novel Bayesian linear regression that includes the mean low water values as a predictor in the model. We validate the re-created MSL dataset and show its consistency with other nearby tide gauge datasets. Using our new corrected dataset, we estimate a rate of sea level rise of 1.1 mm yr−1 during 1953–2016 (95 % credible interval from 0.6 to 1.6 mm yr−1), and a rate of 7 mm yr−1 during 1997–2016 (95 % credible interval from 5 to 8.8 mm yr−1). The overall sea level rise is in line with expected trends, but large multidecadal variability has led to higher rates of rise in recent years.

Background The trading of individual animal genotype information often involves only the exchange of the called genotypes and not necessarily the additional information required to effectively call structural variants. The main aim here was to determine if it is possible to impute copy number variants (CNVs) using the flanking single nucleotide polymorphism (SNP) haplotype structure in cattle. While this objective was achieved using high-density genotype panels (i.e., 713,162 SNPs), a secondary objective investigated the concordance of CNVs called with this high-density genotype panel compared to CNVs called from a medium-density panel (i.e., 45,677 SNPs in the present study). This is the first study to compare CNVs called from high-density and medium-density SNP genotypes from the same animals. High (and medium-density) genotypes were available on 991 Holstein-Friesian, 1015 Charolais, and 1394 Limousin bulls. The concordance between CNVs called from the medium-density and high-density genotypes were calculated separately for each animal. A subset of CNVs which were called from the high-density genotypes was selected for imputation. Imputation was carried out separately for each breed using a set of high-density SNPs flanking the midpoint of each CNV. A CNV was deemed to be imputed correctly when the called copy number matched the imputed copy number. Results For 97.0% of CNVs called from the high-density genotypes, the corresponding genomic position on the medium-density of the animal did not contain a called CNV. The average accuracy of imputation for CNV deletions was 0.281, with a standard deviation of 0.286. The average accuracy of imputation of the CNV normal state, i.e. the absence of a CNV, was 0.982 with a standard deviation of 0.022. Two CNV duplications were imputed in the Charolais, a single CNV duplication in the Limousins, and a single CNV duplication in the Holstein-Friesians; in all cases the CNV duplications were incorrectly imputed. Conclusion The vast majority of CNVs called from the high-density genotypes were not detected using the medium-density genotypes. Furthermore, CNVs cannot be accurately predicted from flanking SNP haplotypes, at least based on the imputation algorithms routinely used in cattle, and using the SNPs currently available on the high-density genotype panel.

The food industry requires automatic methods to establish authenticity of food products. In this work, we address the problem of the certification of suckling lamb meat with respect to the rearing system. We evaluate the performance of neural network classifiers as well as different dimensionality reduction techniques, with the aim of categorizing lamb fat by means of spectroscopy and analyzing the features with more discrimination power. Assessing the stability of feature ranking algorithms also becomes particularly important. We assess six feature selection techniques (χ2, Information Gain, Gain Ratio, Relief and two embedded techniques based on the decision rule 1R and SVM (Support Vector Machine). Additionally, we compare them with common approaches in the chemometrics field like the Partial Least Square (PLS) model and Principal Component Analysis (PCA) regression. Experimental results with a fat sample dataset collected from carcasses of suckling lambs show that performing feature selection contributes to classification performance increasing accuracy from 89.70% with the full feature set to 91.80% and 93.89% with the SVM approach and PCA, respectively. Moreover, the neural classifiers yield a significant increase in the accuracy with respect to the PLS model (85.60% accuracy). It is noteworthy that unlike PCA or PLS, the feature selection techniques that select relevant wavelengths allow the user to identify the regions in the spectrum with the most discriminant power, which makes the understanding of this process easier for veterinary experts. The robustness of the feature selection methods is assessed via a visual approach.

Introduction: Emile Durkheim differentiated between two types of wars: National and Civil Wars in terms of effect on suicide mortality. This study investigates Durkheim’s assertion by examining the effect of Irish Civil War on the 1882-1928 suicide rates trend. Method: We used Auto-Regressive Integrated Moving Average with Explanatory variables (ARIMAX) design adopting Bayesian approach. Results: The odds for death by suicide for the total Irish population during the civil war period were calculated as 0.932 (95% CI: 0.753 to 1.125). This translates to a reduction in the suicide rates by 6.7% (95% CI: 24.7% to -12.5%). The odds for death by suicide for the total Irish population during the First World War period were calculated as 0.872 (95% CI: 0.754 to 0.997). This indicates to a reduction in the suicide rates by 12.8% (95% CI: 24.6% to 0.3%). Conclusion: Evidence from this study support a significant drop in terms of the intensity of suicidal behaviour in the Irish population during World War I more so than during the Irish Civil War. Conflict of interest: none Keywords: suicide, Ireland, Irish Civil war, World War 1, Durkheim’s theory

Conservation paleobiology seeks to leverage proxy reconstructions of ecological com- munities and environmental conditions to predict future changes and inform management decisions. Populations of East African megafauna likely changed during the Holocene in response to trends and events in the regional hydroclimate, but reconstructing these pop- ulations requires development of new proxies. We examine if fecal steroids are a viable proxy for megafauna populations since they are well preserved in sedimentary archives. We measured eleven fecal steroids in 87 fresh dung samples representing 22 species of megafauna in the Maasai Mara National Reserve (Kenya) and a further seven samples from captive animals. Using this reference library, four distinctive groups are identified, which reflect diet and biochemical modification of these inputs during digestion by the gut microbiome. Carnivore dung is characterized by more than ~ 75% cholesterol and pri- mate dung includes uniquely high proportions of coprostanol. Two groups of herbivore are distinguished by their differing proportions of phytosterols that are consumed by eating plants and 5β-stanols produced during digestion. Under cross validation a random forests statistical model accurately classified 72% of dung samples to the species level using fecal steroids. Variability among individuals and between wild and captive animals suggests that fecal steroids in herbivore dung may reflect diversity and variability in diet, while a lack of variability in carnivore dung indicates that they cannot be identified to the species level in most instances. Our results suggest that fecal steroids may have utility in reconstructing the time-evolving composition megafauna populations in East Africa.

Stable isotope ratios are used to reconstruct animal diet in trophic ecology via mixing models. Several assumptions of stable isotope mixing models are critical, i.e., constant trophic discrimination factor and isotopic equilibrium between the consumer and its diet. The isotopic turnover rate (λ and its counterpart the half-life) affects the dynamics of isotopic incorporation for an organism and the isotopic equilibrium assumption: λ involves a time lag between the real assimilated diet and the diet estimated by mixing models at the individual scale. Current stable isotope mixing model studies consider neither this time lag nor even the dynamics of isotopic ratios in general. We developed a mechanistic framework using a dynamic mixing model (DMM) to assess the contribution of λ to the dynamics of isotopic incorporation and to estimate the bias induced by neglecting the time lag in diet reconstruction in conventional static mixing models (SMMs). The DMM includes isotope dynamics of sources (denoted δs), λ and frequency of diet-switch (ω). The results showed a significant bias generated by the SMM compared to the DMM (up to 50% of differences). This bias can be strongly reduced in SMMs by averaging the isotopic variations of the food sources over a time window equal to twice the isotopic half-life. However, the bias will persist (∼15%) for intermediate values of the ω/λ ratio. The inferences generated using a case study highlighted that DMM enhanced estimates of consumer’s diet, and this could avoid misinterpretation in ecosystem functioning, food-web structure analysis and underlying biological processes.

We provide an updated sea level dataset for Dublin for the period 1938 to 2016 at yearly resolution. Using a newly collated sea level record for Dublin Port, as well as two nearby tide gauges at Arklow and Howth Harbour, we perform data quality checks and calibration of the Dublin Port record by adjusting the biased high water level measurements that affect the overall calculation of mean sea level (MSL). To correct these MSL values, we use a novel Bayesian linear regression that includes the Mean Low Water values as a predictor in the model. We validate the re-created MSL dataset and show its consistency with other nearby tide gauge datasets. Using our new corrected dataset, we estimate a rate of 1.08 mm/yr sea level rise at Dublin Port between 1953–2016 (95 % CI from 0.62 to 1.55 mm/yr), and a rate of 6.48 mm/yr between 1997–2016 (95 % CI 4.22 to 8.80 mm/yr). Overall sea level rise is in line with expected trends but large multidecadal varaibility has led to higher rates of rise in recent years.

Estimating population size in space and time is essential for applied ecology and wildlife management purposes; however, making accurate and precise estimates at large scales is highly challenging. An example is the European badger (Meles meles), a widespread and abundant mammal in Ireland. Due to their role in the epidemiology of bovine tuberculosis, the species has been culled in agriculturally dominant landscapes with the intention of reducing spillback infection to local cattle populations. Despite several studies using different approaches having estimated badger populations at different time points and scales, there remains considerable uncertainty regarding the current population and its future trajectory. To explore this uncertainty, we use published data and expert opinion to estimate a snapshot of probable badger population size using a Monte Carlo approach, incorporating variation in three key components: social group numbers, group size, and culling efficacy. Using this approach, we estimate what the badger population in Ireland would be with/without culling, assuming a steady-state population at carrying capacity, and discuss the limitations of our current understanding. The mean estimate for the badger population size was 63,188 (5–95th percentile, 48,037–79,315). Population estimates were sensitive to the assumption of mean group size across landscape type. Assuming a cessation of culling (in favour of vaccination, for example) in agricultural areas, the mean estimated population size was 92,096 (5–95th percentile, 67,188–118,881). Despite significant research being conducted on badgers, estimates on population size at a national level in Ireland are only approximate, which is reflected in the large uncertainty in the estimates from this study and inconsistencies between recording of data parameters in previous studies. Focusing on carefully estimating group size, factors impacting its variation, in addition to understanding the dynamics of repopulation post-culling, could be a fruitful component to concentrate on to improve the precision of future estimates.

International audience; The Sasanian period (224–651 CE) marked an era of large-scale urban projects in southwest Asia, including Iran's semi-arid highlands, with particular efforts to manipulate water bodies. This study presents a recent interdisciplinary investigation of a spring-fed pond at the entrance of the Palace of Ardashir (Firuzabad plain, southwest Iran), part of a recently registered World Heritage site. Historical accounts suggest that the entire water system of the plain, including the pond, underwent a hydraulic re-organization at the beginning of the Sasanian period, a fact that has never been investigated geoarchaeologically. A series of sediment cores were retrieved from the pond to probe its evolution and examine the extent of its landscape modification. The cores were sedimentologically described and radiocarbon-dated with age–depth models established based on 57 AMS (accelerator mass spectrometry) 14C dates to understand the basin's depositional history. The results indicate that (i) Ardashir Pond has existed as part of a larger wetland complex since at least 4500 years ago, (ii) it was substantially enlarged at the beginning of the Sasanian era, and (iii) it was abandoned at the end of the Sasanian period. The Ardashir Pond is one of the first geoarchaeologically investigated case studies to demonstrate the Sasanian landscape in the framework of the “Iranshahr” sociopolitical concept.

Significance We demonstrate that a tsunami in the late 14th century CE destroyed coastal sites along a critical part of the maritime Silk Road and set in motion profound changes in the political economy of Southeast Asia. Our results provide a precise chronology of settlement and trade along a historically strategic section of the Sumatran coast and are robust physical evidence for the rise of the Aceh Sultanate. Tragically, coastal areas impacted by the late 14th century tsunami were devastated by the 2004 Indian Ocean tsunami. This makes our findings relevant to debates about hazard mitigation and risk reduction. This example shows that archaeological, historical, and geological data are relevant in discussions about the long-term sustainability of communities exposed to geological hazards., Archaeological evidence shows that a predecessor of the 2004 Indian Ocean tsunami devastated nine distinct communities along a 40-km section of the northern coast of Sumatra in about 1394 CE. Our evidence is the spatial and temporal distribution of tens of thousands of medieval ceramic sherds and over 5,000 carved gravestones, collected and recorded during a systematic landscape archaeology survey near the modern city of Banda Aceh. Only the trading settlement of Lamri, perched on a headland above the reach of the tsunami, survived into and through the subsequent 15th century. It is of historical and political interest that by the 16th century, however, Lamri was abandoned, while low-lying coastal sites destroyed by the 1394 tsunami were resettled as the population center of the new economically and politically ascendant Aceh Sultanate. Our evidence implies that the 1394 tsunami was large enough to impact severely many of the areas inundated by the 2004 tsunami and to provoke a significant reconfiguration of the region’s political and economic landscape that shaped the history of the region in subsequent centuries.

We present archaeological evidence for a trading settlement dating from the 13th to the mid-16th century ce on an elevated headland in Lamreh village about 30 km east of Banda Aceh, on the northern coast of Sumatra, Indonesia. We propose this site was part of historic Lamri, known from documentary sources as an important node in the maritime “silk road” between the 9th to 16th centuries CE. Our landscape archaeological survey revealed large concentrations of ceramics on the headland that span from the early 13th through the mid-16th century, some of them of imperial quality. Several of the Muslim grave markers at this site are of a distinctive type and date across nearly the entire range of the 15th century. Geological evidence suggests low-lying parts of Lamri were destroyed by a major tsunami at the end of the 14th century. However, our data show that activity on the elevated headland continued until the site was abandoned in the mid-16th century. The lack of material culture dating from the 9th to 13th centuries suggests that earlier textual references to Lamri referred more generally to a broad stretch of the north Sumatran coast, with the headland in Lamreh village emerging as the geographic centre of historic Lamri after the turn of the 13th century., Nous présentons les preuves archéologiques d’un établissement commercial datant du XIIIe au milieu du XVIe siècle EC sur un promontoire élevé du village de Lamreh, à environ 30 km à l’est de Banda Aceh, sur la côte nord de Sumatra, en Indonésie. Nous l’identifions au site historique de Lamri, décrit par les sources textuelles comme un noeud important de la « route de la soie » maritime entre le IXe et le XVIe siècle EC. Notre prospection a révélé de grandes concentrations de céramiques sur le promontoire, qui datent du début du XIIIe siècle jusqu’au milieu du XVIe siècle, certaines de qualité impériale. Plusieurs des stèles funéraires musulmanes de ce site sont d’un type particulier et datent de la quasi-totalité du XVe siècle. Les données géologiques suggèrent que les parties basses de Lamri ont été détruites par un fort tsunami à la fin du XIVe siècle. Cependant, nos données montrent que l’activité sur le promontoire s’est poursuivie jusqu’à ce que le site soit abandonné au milieu du XVIe siècle. L’absence de culture matérielle datant du IXe au XIIIe siècle suggère que les références textuelles antérieures à Lamri se référaient plus généralement à une grande partie de la côte nord de Sumatra, le village de Lamreh ne devenant le centre géographique du Lamri historique qu’après le tournant du XIIIe siècle., Daly Patrick t., McKinnon E.E., Feener R.Michael, Yew Seng Tai, Ardiansyah , Parnell Andrew, Nizamuddin , Ismail Nazli, Sieh Kerry, Majewski Jedrzej. The Historic Trading Port of Lamri on the North Sumatran Coast. In: Bulletin de l'Ecole française d'Extrême-Orient. Tome 105, 2019. pp. 115-144.

We propose a new class of models for the estimation of genotype by environment (GxE) interactions in plant-based genetics. Our approach, named AMBARTI, uses semi-parametric Bayesian additive regression trees to accurately capture marginal genotypic and environment effects along with their interaction in a cut Bayesian framework. We demonstrate that our approach is competitive or superior to similar models widely used in the literature via both simulation and a real world dataset. Furthermore, we introduce new types of visualisation to properly assess both the marginal and interactive predictions from the model. An R package that implements our approach is available athttps://github.com/ebprado/ambarti.

Variable importance, interaction measures, and partial dependence plots are important summaries in the interpretation of statistical and machine learning models. In this paper we describe new visualization techniques for exploring these model summaries. We construct heatmap and graph-based displays showing variable importance and interaction jointly, which are carefully designed to highlight important aspects of the fit. We describe a new matrix-type layout showing all single and bivariate partial dependence plots, and an alternative layout based on graph Eulerians focusing on key subsets. Our new visualizations are model-agnostic and are applicable to regression and classification supervised learning settings. They enhance interpretation even in situations where the number of variables is large. Our R package vivid (variable importance and variable interaction displays) provides an implementation., Comment: 31 pages, 10 figures

We analysed multiple tide gauges from the east coast of Ireland over the period 1938–2018. We validated the different time series against each other and performed a missing value imputation exercise, which enabled us to produce a homogenised record. The recordings of all tide gauges were found to be in good agreement between 2003–2015, though this was markedly less so from 2016 to the present. We estimate the sea level rise in Dublin port for this period at 10 mm yr−1. The rate over the longer period of 1938–2015 was 1.67 mm yr−1 which is in good agreement with the global average. We found that the rate of sea level rise in the longer term record is cyclic with some extreme upward and downward trends. However, starting around 1980, Dublin has seen significantly higher rates that have been always positive since 1996, and this is mirrored in the surrounding gauges. Furthermore, our analysis indicates an increase in sea level variability since 1980. Both decadal rates and continuous time rates are calculated and provided with uncertainties in this paper.

Stable isotope mixing models are regularly used to provide probabilistic estimates of source contributions to dietary mixtures. Whilst Bayesian implementations of isotope mixing models have become prominent, the use of appropriate diet-tissue discrimination factors (DTDFs) remains as the least resolved aspect. The DTDFs are critical in providing accurate inferences from these models. Using both simulated and laboratory-based experimental data, this study provides conceptual and practical applications of isotope mixing models by exploring the role of DTDFs. The experimental study used Mozambique Tilapia Oreochromis mossambicus, a freshwater fish, to explore multi-tissue variations in isotopic incorporation patterns, and to evaluate isotope mixing model outputs based on the experiment- and literature-based DTDFs. Isotope incorporation patterns were variable for both muscle and fin tissues among the consumer groups that fed diet sources with different stable isotope values. Application of literature-based DTDFs in isotope mixing models consistently underestimated the dietary proportions of all single-source consumer groups. In contrast, application of diet-specific DTDFs provided better dietary estimates for single-source consumer groups. Variations in the proportional contributions of the individual sources were, nevertheless, observed for the mixed-source consumer group, which suggests that isotope assimilation of the individual food sources may have been influenced by other underlying physiological processes. This study provides evidence that stable isotope values from different diet sources exhibit large variations as they become incorporated into consumer tissues. This suggests that the application of isotope mixing models requires consideration of several aspects such as diet type and the associated biological processes that may influence DTDFs.

ObjectivesTo identify serum protein biomarkers which might separate early inflammatory arthritis (EIA) patients with psoriatic arthritis (PsA) from those with rheumatoid arthritis (RA) to provide an accurate diagnosis and support appropriate early intervention.MethodsIn an initial protein discovery phase, the serum proteome of a cohort of patients with PsA and RA was interrogated using unbiased liquid chromatography mass spectrometry (LC-MS/MS) (n=64 patients), a multiplexed antibody assay (Luminex) for 48 proteins (n=64 patients) and an aptamer-based assay (SOMAscan) targeting 1,129 proteins (n=36 patients). Subsequently, analytically validated targeted multiple reaction monitoring (MRM) assays were developed to further evaluate those proteins identified as discriminatory during the discovery. During an initial verification phase, MRM assays were developed to a panel of 150 proteins (by measuring a total of 233 peptides) and used to re-evaluate the discovery cohort (n=60). During a second verification phase, the panel of proteins was expanded to include an additional 23 proteins identified in other proteomic discovery analyses of arthritis patients. The expanded panel was evaluated using a second, independent cohort of PsA and RA patients (n=167).ResultsMultivariate analysis of the protein discovery data revealed that it was possible to discriminate PsA from RA patients with an area under the curve (AUC) of 0.94 for nLC-MS/MS, 0.69 for Luminex based measurements; 0.73 for SOMAscan analysis. During the initial verification phase, random forest models confirmed that proteins measured by MRM could differentiate PsA and RA patients with an AUC of 0.79 and during the second phase of verification the expanded panel could segregate the two disease groups with an AUC of 0.85.ConclusionWe report a serum protein biomarker panel which can separate EIA patients with PsA from those with RA. We suggest that the routine use of such a panel in EIA patients will improve clinical decision making and with continued evaluation and refinement using additional patient cohorts will support the development of a diagnostic test for patients with PsA.

Background Despite an epidemic increase in the prevalence of Human Papilloma Virus (HPV) related Oropharyngeal Squamous Cell Carcinomas (OPSCCs) in Northern America and parts of Europe, there is virtually no information about the natural history of these cancers. The lack of well–defined precursor lesions and limited data on oral HPV persistence and clearance rates, poses a challenge for disease modelling. We propose a novel mathematical modelling approach to estimate the conditional probability of developing HPV related OPSCCs following a prevalent HPV infection and other covariates. Methods We developed a double-Bayes method, whereby a Bayesian machine learning model first estimates the probability of an individual having an oral HPV infection, given OPSCC and other covariate information. The model is then inverted using Bayes’ theorem to reverse the probability relationship. The mathematical model was derived from two datasets representing the adult population in the United States (US), the Surveillance Epidemiology and End Results Program (SEER) Head and Neck with HPV Status Database and the National Health and Nutrition Examination Survey (NHANES) 2011-2014. Results The model dataset contains 8,623 subjects of which 70.7% had a prevalent oral HPV positive infection. When stratified by age, sex, marital status and race/ethnicity, the model estimated higher conditional probability for developing OPSCCs following an oral HPV infection in non-Hispanic White males and females compared to other race/ethnicities. Non-Hispanic White males with an oral HPV infection had nearly two fold higher risk of developing OPSCC than non-Hispanic White females (10.6 cases per 10,000 thousand vs 5.05 cases per 10,000) in the age range 50-60. Conclusion We have employed a novel statistical approach to estimate the conditional probability of developing OPSCCs following an oral HPV infection and covariates age, sex, ethnicity and marital status in the US population. We recognise that at best this is a first guess estimate of a natural history model of HPV driven OPSCCs within the existing limitations of the model.