Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas.

BACKGROUND: Volume of interest (VOI) segmentation is a crucial step for Radiomics analyses and radiotherapy (RT) treatment planning. Because it can be time-consuming and subject to inter-observer variability, we developed and tested a Deep Learning-based automatic segmentation (DLBAS) algorithm to reproducibly predict the primary gross tumor as VOI for Radiomics analyses in extremity soft tissue sarcomas (STS). METHODS: A DLBAS algorithm was trained on a cohort of 157 patients and externally tested on an independent cohort of 87 patients using contrast-enhanced MRI. Manual tumor delineations by a radiation oncologist served as ground truths (GTs). A benchmark study with 20 cases from the test cohort compared the DLBAS predictions against manual VOI segmentations of two residents (ERs) and clinical delineations of two radiation oncologists (ROs). The ROs rated DLBAS predictions regarding their direct applicability. RESULTS: The DLBAS achieved a median dice similarity coefficient (DSC) of 0.88 against the GTs in the entire test cohort (interquartile range (IQR): 0.11) and a median DSC of 0.89 (IQR 0.07) and 0.82 (IQR 0.10) in comparison to ERs and ROs, respectively. Radiomics feature stability was high with a median intraclass correlation coefficient of 0.97, 0.95 and 0.94 for GTs, ERs, and ROs, respectively. DLBAS predictions were deemed clinically suitable by the two ROs in 35% and 20% of cases, respectively. CONCLUSION: The results demonstrate that the DLBAS algorithm provides reproducible VOI predictions for radiomics feature extraction. Variability remains regarding direct clinical applicability of predictions for RT treatment planning.

The predicted benefits of genomic selection on pig breeding objectives.

The premise was tested that the additional genetic gain was achieved in the overall breeding objective in a pig breeding program using genomic selection (GS) compared to a conventional breeding program, however, some traits achieved larger gain than other traits. GS scenarios based on different reference population sizes were evaluated. The scenarios were compared using a deterministic simulation model to predict genetic gain in scenarios with and without using genomic information as an additional information source. All scenarios were compared based on selection accuracy and predicted genetic gain per round of selection for objective traits in both sire and dam lines. The results showed that GS scenarios increased overall response in the breeding objectives by 9% to 56% and 3.5% to 27% in the dam and sire lines, respectively. The difference in response resulted from differences in the size of the reference population. Although all traits achieved higher selection accuracy in GS, traits with limited phenotypic information at the time of selection or with low heritability, such as sow longevity, number of piglets born alive, pre- and post-weaning survival, as well as meat and carcass quality traits achieved the largest additional response. This additional response came at the expense of smaller responses for traits that are easy to measure, such as back fat and average daily gain in GS compared to the conventional breeding program. Sow longevity and drip loss percentage did not change in a favourable direction in GS with a reference population of 500 pigs. With a reference population of 1000 pigs or onwards, sow longevity and drip loss percentage began to change in a favourable direction. Despite the smaller responses for average daily gain and back fat thickness in GS, the overall breeding objective achieved additional gain in GS.

Genetic diversity, population structure and origin of the native goats in Central Laos.

Maintaining genetic diversity and variation in livestock populations is critical for natural and artificial selection promoting genetic improvement while avoiding problems due to inbreeding. In Laos, there are concerns that there has been a decline in genetic diversity and a rise in inbreeding among native goats in their village-based smallholder system. In this study, we investigated the genetic diversity of Lao native goats in Phin, Songkhone and Sepon districts in Central Laos for the first time using Illumina's Goat SNP50 BeadChip. We also explored the genetic relationships between Lao goats with 163 global goat populations from 36 countries. Our results revealled a close genetic relationship between Lao native goats and Chinese, Mongolian and Pakistani goats, sharing ancestries with Guangfen, Jining Grey and Luoping Yellow breeds (China) and Teddi goats (Pakistan). The observed (Ho) and expected (He) heterozygosity were 0.292 and 0.303 (Laos), 0.288 and 0.288 (Sepon), 0.299 and 0.308 (Phin) and 0.289 and 0.305 (Songkhone), respectively. There was low to moderate genetic differentiation (FST: 0.011-0.043) and negligible inbreeding coefficients (FIS: -0.001 to 0.052) between goat districts. The runs of homozygosity (ROH) had an average length of 5.92-6.85 Mb, with short ROH segments (1-5 Mb length) being the most prevalent (66.34%). Longer ROH segments (20-40 and >40 Mb length categories) were less common, comprising only 4.81% and 1.01%, respectively. Lao goats exhibit moderate genetic diversity, low-inbreeding levels and adequate effective population size. Some genetic distinctions between Lao goats may be explained by geographic and cultural features.

Coronary Artery Calcium Scoring Using Virtual Versus True Noncontrast Images From Photon-Counting Coronary CT Angiography.

Background Coronary artery calcium scoring (CACS) for coronary artery disease requires true noncontrast (TNC) CT alongside contrast-enhanced coronary CT angiography (CCTA). Photon-counting CT provides an algorithm (PureCalcium) for reconstructing virtual noncontrast images from CCTA specifically for CACS. Purpose To assess CACS differences based on PureCalcium images derived from contrast-enhanced photon-counting CCTA compared with TNC images and evaluate the impact of these differences on the clinically relevant classification of patients into plaque burden groups. Materials and Methods Photon-counting CCTA images acquired between August 2022 and May 2023 were retrospectively identified. Agatston scores were derived from both TNC and PureCalcium images and tested for differences with use of the Wilcoxon signed-rank test. The agreement was assessed with use of equivalence tests, Bland-Altman analysis, and intraclass correlation coefficient. Plaque burden groups were established based on Agatston scores, and agreement was evaluated using weighted Cohen kappa. The dose-length product was analyzed. Results Among 170 patients (mean age, 63 years ± 13 [SD]; 92 male), 111 had Agatston scores higher than 0. Median Agatston scores did not differ between TNC and PureCalcium images (4.8 [IQR, 0-84.4; range, 0.0-2151.8] vs 2.7 [IQR, 0-90.7; range, 0.0-2377.1]; P = .99), with strong correlation (intraclass correlation coefficient, 0.98 [95% CI: 0.97, 0.99]). The equivalence test was inconclusive, with a 95% CI of 0.90, 1.19. Bland-Altman analysis showed wide repeatability limits, indicating low agreement between the two scores. With use of the PureCalcium algorithm, 125 of 170 patients (74%) were correctly classified into plaque burden groups (excellent agreement, κ = 0.88). Patients without plaque burden were misclassified at higher than normal rates (P < .001). TNC image acquisition contributed a mean of 19.7% ± 8.8 of the radiation dose of the entire examination. Conclusion PureCalcium images show potential to replace TNC images for measuring Agatston scores, thereby reducing radiation dose in CCTA. There was strong correlation in calcium scores between TNC and PureCalcium, but limited agreement. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Sakuma in this issue.

Validation of reaction norm breeding values for robustness in Australian sheep.

BACKGROUND: There can be variation between animals in how stable their genetic merit is across different environments due to genotype-by-environment (G×E) interactions. This variation could be used in breeding programs to select robust genotypes that combine high overall performance with stable genetic ranking across environments. There have been few attempts to validate breeding values for robustness in livestock, although this is a necessary step towards their implementation in selection decisions. The objective of this study was to validate breeding values for the robustness of body weight across different growth environments that were estimated using reaction norm models in sheep data. RESULTS: Using threefold cross-validation for the progeny of 337 sires, the average correlation between single-step breeding values for the reaction norm slope and the realised robustness of progeny across different growth environments was 0.21. The correlation between breeding values for the reaction slope estimated independently in two different datasets linked by common sires was close to the expected correlation based on theory. CONCLUSIONS: Slope estimated breeding values (EBV) obtained using reaction norm models were predictive of the phenotypic robustness of progeny across different environments and were consistent for sires with progeny in two different datasets. Selection based on reaction norm EBV could be used to increase the robustness of a population to environmental variation.

Minimizing Contrast Media Dose in CT Pulmonary Angiography with Clinical Photon Counting Using High Pitch Technique.

RATIONALE AND OBJECTIVES: To evaluate the potential to reduce the amount of iodinated contrast media (CM) for computer tomographic pulmonary angiography (CTPA) with a novel photon-counting-detector CT (PCCT). MATERIALS AND METHODS: Overall, 105 patients referred for CTPA were retrospectively included in this study. CTPA was performed using bolus tracking and high-pitch dual-source scanning (FLASH mode) on a novel PCCT (Naeotom Alpha, Siemens Healthineers). CM (Accupaque 300, GE Healthcare) dose was lowered stepwise following the introduction of the new CT scanner. Thus, patients could be divided into 3 groups as follows: group 1, n = 29, 35 ml of CM; group 2, n = 62, 45 ml of CM and group 3, n = 14, 60 ml of CM. Four readers independently assessed the image quality (Likert-scale 1-5) and adequate assessment of the segmental pulmonary arteries. Additionally, the pulmonary arterial contrast opacification was measured. RESULTS: The subjective image quality was rated highest in group 1 with 4.6 compared to 4.5 (group 2) and 4.1 (group 3) with a significant difference between groups 1 and 3 (p < 0.001) and between groups 2 and 3 (p = 0.003). In all groups, almost all segmental pulmonary arteries could be assessed adequately without significant differences (18.5 vs. 18.7 vs. 18.4). Mean attenuation in the pulmonary trunk did not differ significantly between groups 321 ± 92 HU versus 345 ± 93 HU versus 347 ± 88 HU (p = 0.69). CONCLUSION: Significant CM dose reduction is possible without a reduction in image quality. PCCT enables diagnostic CTPA with 35 ml of CM.

Evaluation of the accuracy of the Asanté assay as a point-of-care rapid test for HIV-1 recent infections using serum bank specimens from blood donors in South Africa, July 2018 - August 2021.

BACKGROUND: Point-of-care (POC) rapid recency testing can be used as a cost-effective tool to identify recently infected individuals (i.e. infected within the last 12 months) in near-real time, support epidemic control and identify hotspots for transmission as part of recent infection surveillance. OBJECTIVE: To evaluate the performance of the Asanté (HIV-1) rapid recency assay as a POC rapid test among blood donors in South Africa (SA). METHODS: The study was a cross-sectional and validity study of the Asanté HIV-1 Rapid Recency Assay performed on 715 consecutively archived plasma donor specimens from the SA National Blood Services to determine their recency and established HIV infection status. ELISA and rapid assays for HIV antibody detection were used as the reference-testing standard for confirming an infection, while the Maxim HIV-1 limiting antigen (LAg) avidity assay was used as a reference for comparing HIV recency status. Validity tests (sensitivity, specificity, negative and positive predictive values) and Cohen-Kappa tests of the agreement were conducted to compare the Asanté HIV-1 rapid recency assay results with the reference tests. RESULTS: Of the 715 studied blood samples, 63.1% (n=451/715) were confirmed to be HIV-positive based on the reference standard. The sensitivity and specificity of the Asanté HIV-1 rapid recency assay in diagnosing established HIV infection compared to the ELISA were 98.4% (95% CI 96.7 - 99.3) and 99.6% (95% CI 97.6 - 100), respectively. Compared with HIV rapid assay, the sensitivity and specificity of the Asanté HIV-1 rapid recency assay was 98.7% (95% CI 97.0 - 99.4) and 99.2% (95% CI 97.1 - 100), respectively. Of the 451 HIV-positive blood samples, 43% were confirmed as recent HIV infections by the Maxim HIV-1 LAg avidity assay. There was high agreement between the Asanté HIV-1 rapid recency assay and the Maxim HIV-1 LAg avidity assay (94.1%, k=0.879, p<0.0001). The sensitivity and specificity of the Asante HIV-1 assay was 89.4% (95% CI 84.0 - 93.0) and 97.7% (95% CI 94.8 - 99.0), respectively. CONCLUSION: The Asanté HIV-1 rapid recency assay test results demonstrated high accuracy (>90%) compared with the HIV ELISA and rapid assays for determining established infection and the Maxim HIV-1 LAg avidity assay for classifying recent HIV-1 infections. The assay's sensitivity for established infections was below the World Health Organization criteria (<99%) for POC devices. The Asanté HIV-1 rapid recency assay can be used to distinguish between recent and long-term infections, but may not be considered a POC test for determining HIV infection.

An effective hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation.

The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter <1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor, α, which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase prediction accuracy, in addition to blending and tuning processes, when using HBLUP.

Partitioning the forms of genotype-by-environment interaction in the reaction norm analysis of stability.

KEY MESSAGE: The reaction norm analysis of stability can be enhanced by partitioning the contribution of different types of G × E to the variation in slope. The slope of regression in a reaction norm model, where the performance of a genotype is regressed over an environmental covariable, is often used as a measure of stability of genotype performance. This method could be developed further by partitioning variation in the slope of regression into the two sources of genotype-by-environment interaction (G × E) which cause it: scale-type G × E (heterogeneity of variance) and rank-type G × E (heterogeneity of correlation). Because the two types of G × E have very different properties, separating their effect would enable a clearer understanding of stability. The aim of this paper was to demonstrate two methods which seek to achieve this in reaction norm models. Reaction norm models were fit to yield data from a multi-environment trial in Barley (Hordeum vulgare), with the adjusted mean yield from each environment used as the environmental covariable. Stability estimated from factor-analytic models, which can disentangle the two types of G × E and estimate stability based on rank-type G × E, was used for comparison. Adjusting the reaction norm slope to account for scale-type G × E using a genetic regression more than tripled the correlation with factor-analytic estimates of stability (0.24-0.26 to 0.80-0.85), indicating that it removed variation in the reaction norm slope that originated from scale-type G × E. A standardisation procedure had a more modest increase (055-0.59) but could be useful when curvilinear reaction norms are required. Analyses which use reaction norms to explore the stability of genotypes could gain additional insight into the mechanisms of stability by applying the methods outlined in this study.

The Journey from Endoscopic Submucosal Dissection to Third Space Endoscopy.

With the advent of endoscopic submucosal dissection, a variety of endoscopic devices including knives and high-frequency electrosurgical unit have become available. In addition, the concept of natural orifice transluminal endoscopic surgery pushed flexible endoscopic surgery ahead. In this review, the birth of peroral endoscopic myotomy and its expansion into the field of submucosal endoscopy are reviewed.

Profit inefficiency of goat farming in Malawi: A Bayesian approach.

Goat farms located in villages constitute an important sub-sector to buffer household livelihoods against climate shocks in absence of insurance markets, and thus the need to intensify production to meet the growing goat meat demand. There has been little attempt to quantify the efficiency of goat farms and the associated drivers. Potential initiatives to bolster goat farm incomes have not gone beyond production. Using the Bayesian Stochastic Frontier Approach and Malawi's national Integrated Household Panel Survey data from 2010 to 2019, we analyze the profit efficiency of goat farming and the priority drivers that influence inefficiencies across production plants. The findings reveal that farmers are operating below the profit frontier and 71% of the profit-loss is influenced by farm characteristics. The results further suggest that policies involving stirring commercializing and specialization are key to closing the efficiency gap. Finally, the study proposes new policy directions, namely, government provision of livestock extension services to farmers, incorporating goat farming into women's empowerment programs, and provision of access to tailor-made livestock microfinance loans.

Genome-wide association study for carcass traits in Hanwoo cattle using additional relatives' information of non-genotyped animals.

The aim of this study was to find significant genomic regions associated with carcass traits in Hanwoo cattle and to compare the benefit of using additional information from non-genotyped animals. Imputed whole-genome sequence data were used along with phenotypic data on 13 715 genotyped animals as well as phenotypes of 440 284 non-genotyped animals that were offspring of 454 genotyped sires. For carcass weight, 15 083 SNPs in 33 QTL regions and 313 candidate genes were identified. We found 410 SNPs in 17 QTL regions containing 122 candidate genes for back fat thickness. In total, 656 SNPs in 19 QTLs with 137 candidate genes for eye muscle area and 79 SNPs in 12 QTL regions with 77 candidate genes were identified for marbling score. The most important candidate genes included ZFAT, TG, PLAG1, CHCHD7, and TOX for carcass weight and eye muscle area, NOG for back fat thickness, and EVOVL5 for marbling score. This study showed that the use of phenotypic records on non-genotyped progeny along with imputed whole-genome sequence data increased the power of detecting new significant genomic regions.

Genomic analysis of the slope of the reaction norm for body weight in Australian sheep.

BACKGROUND: Selection of livestock based on their robustness or sensitivity to environmental variation could help improve the efficiency of production systems, particularly in the light of climate change. Genetic variation in robustness arises from genotype-by-environment (G × E) interactions, with genotypes performing differently when animals are raised in contrasted environments. Understanding the nature of this genetic variation is essential to implement strategies to improve robustness. In this study, our aim was to explore the genetics of robustness in Australian sheep to different growth environments using linear reaction norm models (RNM), with post-weaning weight records of 22,513 lambs and 60 k single nucleotide polymorphisms (SNPs). The use of scale-corrected genomic estimated breeding values (GEBV) for the slope to account for scale-type G × E interactions was also investigated. RESULTS: Additive genetic variance was observed for the slope of the RNM, with genetic correlations between low- and high-growth environments indicating substantial re-ranking of genotypes (0.44-0.49). The genetic variance increased from low- to high-growth environments. The heritability of post-weaning body weight ranged from 0.28 to 0.39. The genetic correlation between intercept and slope of the reaction norm for post-weaning body weight was low to moderate when based on the estimated (co)variance components but was much higher when based on back-solved SNP effects. An initial analysis suggested that a region on chromosome 11 affected both the intercept and the slope, but when the GEBV for the slope were conditioned on the GEBV for the intercept to remove the effect of scale-type G × E interactions on SNP effects for robustness, a single genomic region on chromosome 7 was found to be associated with robustness. This region included genes previously associated with growth traits and disease susceptibility in livestock. CONCLUSIONS: This study shows a significant genetic variation in the slope of RNM that could be used for selecting for increased robustness of sheep. Both scale-type and rank-type G × E interactions contributed to variation in the slope. The correction for scale effects of GEBV for the slope should be considered when analysing robustness using RNM. Overall, robustness appears to be a highly polygenic trait.

Economic values for production, fertility and mastitis traits for temperate dairy cattle breeds in tropical Sri Lanka.

Economic values for annual milk yield (MY, kg), annual fat yield (FY, kg), annual protein yield (PY, kg), age at first calving (AFC, days), number of services per conception (NSC), calving interval (CI, days) and mastitis episodes (MS) were derived for temperate dairy cattle breeds in tropical Sri Lanka using a bio-economic model. Economic values were calculated on a per cow per year basis. Derived economic values in rupees (LKR) for MY, FY and PY were 107, -162 and -15, while for AFC, NSC, CI and MS, economic values were -59, -270, -84 and -8,303. Economic values for FY and PY further decreased with higher feed prices, and a less negative economic value for FY was obtained with increased price for fat. Negative economic values for FY and PY show that genetic improvement for these traits is not economical due to the high feed costs and/or the insufficient payment for fat and protein. Therefore, revision of milk fat and protein payments is recommended. Furthermore, the breeding objective developed in this study was dominated by milk production and fertility traits. Adaptability and functional traits that are important in a temperate dairy cattle breeding programme in tropical Sri Lanka, such as longevity, feed efficiency, disease resistance and heat tolerance should be recorded to incorporate them in the breeding objective. Continued trait recording of all traits is recommended to ensure dairy cows can be selected more effectively in a tropical environment based on a breeding objective that also includes adaptability and functional traits.

Genomic prediction in a numerically small breed population using prioritized genetic markers from whole-genome sequence data.

The objective of this study was to investigate the accuracy of genomic prediction of body weight and eating quality traits in a numerically small sheep population (Dorper sheep). Prediction was based on a large multi-breed/admixed reference population and using (a) 50k or 500k single nucleotide polymorphism (SNP) genotypes, (b) imputed whole-genome sequencing data (~31 million), (c) selected SNPs from whole genome sequence data and (d) 50k SNP genotypes plus selected SNPs from whole-genome sequence data. Furthermore, the impact of using a breed-adjusted genomic relationship matrix on accuracy of genomic breeding value was assessed. The selection of genetic variants was based on an association study performed on imputed whole-genome sequence data in an independent population, which was chosen either randomly from the base population or according to higher genetic proximity to the target population. Genomic prediction was based on genomic best linear unbiased prediction (GBLUP), and the accuracy of genomic prediction was assessed according to the correlation between genomic breeding value and corrected phenotypes divided by the square root of trait heritability. The accuracy of genomic prediction was between 0.20 and 0.30 across different traits based on common 50k SNP genotypes, which improved on average by 0.06 (absolute value) on average based on using prioritized genetic markers from whole-genome sequence data. Using prioritized genetic markers from a genetically more related GWAS population resulted in slightly higher prediction accuracy (0.02 absolute value) compared to genetic markers derived from a random GWAS population. Using high-density SNP genotypes or imputed whole-genome sequence data in GBLUP showed almost no improvement in genomic prediction accuracy however, accounting for different marker allele frequencies in reference population according to a breed-adjusted GRM resulted to on average 0.024 (absolute value) increase in accuracy of genomic prediction.

Accuracy of Genomic Prediction for Milk Production Traits in Philippine Dairy Buffaloes.

The objective of this study was to compare the accuracies of genomic prediction for milk yield, fat yield, and protein yield from Philippine dairy buffaloes using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with the accuracies based on pedigree BLUP (pBLUP). To also assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted breeding values (BVs) was also calculated. Two data sets were analyzed. The GENO data consisting of all female buffaloes that have both phenotypes and genotypes (n = 904 with 1,773,305-days lactation records) were analyzed using pBLUP and GBLUP. The ALL data, consisting of the GENO data plus females with phenotypes but not genotyped (n = 1,975 with 3,821,305-days lactation records), were analyzed using pBLUP and ssGBLUP. Animals were genotyped with the Affymetrix 90k buffalo genotyping array. After quality control, 60,827 single-nucleotide polymorphisms were used for downward analysis. A pedigree file containing 2,642 animals was used for pBLUP and ssGBLUP. Accuracy of prediction was calculated as the correlation between the predicted BVs of the test set and adjusted phenotypes, which were corrected for fixed effects, divided by the square root of the heritability of the trait, corrected for the number of lactations used in the test set. To assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted BVs was also calculated. Results showed that genomic methods (GBLUP and ssGBLUP) provide more accurate predictions compared to pBLUP. Average GBLUP and ssGBLUP accuracies were 0.24 and 0.29, respectively, whereas average pBLUP accuracies (for GENO and ALL data) were 0.21 and 0.22, respectively. Slopes of the two genomic methods were also closer to one, indicating lesser bias, compared to pBLUP. Average GBLUP and ssGBLUP slopes were 0.89 and 0.84, respectively, whereas the average pBLUP (for GENO and ALL data) slopes were 0.80 and 0.54, respectively.

Central sensitisation in chronic fatigue syndrome and fibromyalgia; a case control study.

INTRODUCTION: Chronic fatigue syndrome (CFS) and fibromyalgia (FM) are both complex conditions that are challenging to treat. This may be related to an incomplete understanding of their pathophysiology, itself obfuscated by their heterogeneity. The symptomatic overlap between them and their common comorbidity suggests a shared vulnerability, which might be explained by central sensitisation. METHODS: 19 CFS cases, 19 FM cases and 20 age and sex matched healthy controls (HC) were recruited primarily from secondary care clinics in London. Those with other pain disorders, psychiatric diagnoses and those taking centrally acting or opiate medications were excluded. Participants were asked to abstain from alcohol and over the counter analgaesia 48 h prior to assessment by static and dynamic quantitative sensory tests, including measures of temporal summation (TS) and conditioned pain modulation (CPM). RESULTS: CS, as defined by the presence of both enhanced TS and inefficient CPM, was present in 16 (84%) CFS cases, 18 (95%) FM cases, and none of the HC (p < 0.001). Pressure pain thresholds were lower in CFS (Median222kPaIQR 146-311; p = 0.04) and FM cases (Median 189 kPa; IQR 129-272; p = 0.003) compared to HC (Median 311 kPa; IQR 245-377). FM cases differed from HC in cold-induced (FM = 22.6 °C (15.3-27.7) vs HC = 14.2 °C (9.0-20.5); p = 0.01) and heat-induced (FM = 38.0 °C (35.2-44.0) vs HC = 45.3 °C (40.1-46.8); p = 0.03) pain thresholds, where CFS cases did not. CONCLUSION: Central sensitisation may be a common endophenotype in chronic fatigue syndrome and fibromyalgia. Further research should address whether central sensitisation is a cause or effect of these disorders.

A conditional multi-trait sequence GWAS discovers pleiotropic candidate genes and variants for sheep wool, skin wrinkle and breech cover traits.

BACKGROUND: Imputation to whole-genome sequence is now possible in large sheep populations. It is therefore of interest to use this data in genome-wide association studies (GWAS) to investigate putative causal variants and genes that underpin economically important traits. Merino wool is globally sought after for luxury fabrics, but some key wool quality attributes are unfavourably correlated with the characteristic skin wrinkle of Merinos. In turn, skin wrinkle is strongly linked to susceptibility to "fly strike" (Cutaneous myiasis), which is a major welfare issue. Here, we use whole-genome sequence data in a multi-trait GWAS to identify pleiotropic putative causal variants and genes associated with changes in key wool traits and skin wrinkle. RESULTS: A stepwise conditional multi-trait GWAS (CM-GWAS) identified putative causal variants and related genes from 178 independent quantitative trait loci (QTL) of 16 wool and skin wrinkle traits, measured on up to 7218 Merino sheep with 31 million imputed whole-genome sequence (WGS) genotypes. Novel candidate gene findings included the MAT1A gene that encodes an enzyme involved in the sulphur metabolism pathway critical to production of wool proteins, and the ESRP1 gene. We also discovered a significant wrinkle variant upstream of the HAS2 gene, which in dogs is associated with the exaggerated skin folds in the Shar-Pei breed. CONCLUSIONS: The wool and skin wrinkle traits studied here appear to be highly polygenic with many putative candidate variants showing considerable pleiotropy. Our CM-GWAS identified many highly plausible candidate genes for wool traits as well as breech wrinkle and breech area wool cover.