The one comparing narrative social network extraction techniques

Analysing narratives through their social networks is an expanding field in quantitative literary studies. Manually extracting a social network from any narrative can be time consuming, so automatic extraction methods of varying complexity have been developed. However, the effect of different extraction methods on the resulting networks is unknown. Here we model and compare three extraction methods for social networks in narratives: manual extraction, co-occurrence automated extraction and automated extraction using machine learning. Although the manual extraction method produces more precise results in the network analysis, it is highly time consuming. The automatic extraction methods yield comparable results for density, centrality measures and edge weights. Our results provide evidence that automatically-extracted social networks are reliable for many analyses. We also describe which aspects of analysis are not reliable with such a social network. Our findings provide a framework to analyse narratives, which help us improve our understanding of how stories are written and evolve, and how people interact with each other. Index Tenns-social networks, narratives, televisionMichelle Edwards, Jonathan Tuke, Matthew Roughan, Lewis Mitchel

Objective measures for the assessment of post-operative pain in bos indicus bull calves following castration

The aim of the study was to assess pain in Bos indicus bull calves following surgical castration. Forty-two animals were randomised to four groups: no castration (NC, n = 6); castration with pre-operative lidocaine (CL, n = 12); castration with pre-operative meloxicam (CM, n = 12); and, castration alone (C, n = 12). Bodyweight was measured regularly and pedometers provided data on activity and rest from day −7 (7 days prior to surgery) to 13. Blood was collected for the measurement of serum amyloid A (SAA), haptoglobin, fibrinogen, and iron on days 0, 3 and 6. Bodyweight and pedometry data were analysed with a mixed effect model. The blood results were analysed with repeated measure one-way analysis of variance (ANOVA). There was no treatment effect on bodyweight or activity. The duration of rest was greatest in the CM group and lowest in the C group. There was a significant increase in the concentrations of SAA, haptoglobin, and fibrinogen in all of the groups from day 0 to 3. Iron concentrations were not different at the time points it was measured. The results of this study suggest that animals rest for longer periods after the pre-operative administration of meloxicam. The other objective assessments measured in this study were not able to consistently differentiate between treatment groups

Sex differences in mechanical allodynia: how can it be preclinically quantified and analyzed?

Extent: 16 p.Translating promising preclinical drug discoveries to successful clinical trials remains a significant hurdle in pain research. Although animal models have significantly contributed to understanding chronic pain pathophysiology, the majority of research has focused on male rodents using testing procedures that produce sex difference data that do not align well with comparable clinical experiences. Additionally, the use of animal pain models presents ongoing ethical challenges demanding continuing refinement of preclinical methods. To this end, this study sought to test a quantitative allodynia assessment technique and associated statistical analysis in a modified graded nerve injury pain model with the aim to further examine sex differences in allodynia. Graded allodynia was established in male and female Sprague Dawley rats by altering the number of sutures placed around the sciatic nerve and quantified by the von Frey test. Linear mixed effects modeling regressed response on each fixed effect (sex, oestrus cycle, pain treatment). On comparison with other common von Frey assessment techniques, utilizing lower threshold filaments than those ordinarily tested, at 1 s intervals, appropriately and successfully investigated female mechanical allodynia, revealing significant sex and oestrus cycle difference across the graded allodynia that other common behavioral methods were unable to detect. Utilizing this different von Frey approach and graded allodynia model, a single suture inflicting less allodynia was sufficient to demonstrate exaggerated female mechanical allodynia throughout the phases of dioestrus and pro-oestrus. Refining the von Frey testing method, statistical analysis technique and the use of a graded model of chronic pain, allowed for examination of the influences on female mechanical nociception that other von Frey methods cannot provide.Lauren Nicotra, Jonathan Tuke, Peter M. Grace, Paul E. Rolan and Mark R. Hutchinso

Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes.

Initial results from sequencing studies suggest that there are relatively few low-frequency (<5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency-large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant-common phenotype associations-11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (<5%), respectively, low frequency-large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P < 1 × 10(-06) (false discovery rate ∼5%)] and one of eight biomarker associations at P < 8 × 10(-10). Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect

Using genetics to understand the causal influence of higher BMI on depression

Background: Depression is more common in obese than non-obese individuals, especially in women, but the causal relationship between obesity and depression is complex and uncertain. Previous studies have used genetic variants associated with BMI to provide evidence that higher body mass index (BMI) causes depression, but have not tested whether this relationship is driven by the metabolic consequences of BMI nor for differences between men and women. Methods: We performed a Mendelian randomization study using 48 791 individuals with depression and 291 995 controls in the UK Biobank, to test for causal effects of higher BMI on depression (defined using self-report and Hospital Episode data). We used two genetic instruments, both representing higher BMI, but one with and one without its adverse metabolic consequences, in an attempt to 'uncouple' the psychological component of obesity from the metabolic consequences. We further tested causal relationships in men and women separately, and using subsets of BMI variants from known physiological pathways. Results: Higher BMI was strongly associated with higher odds of depression, especially in women. Mendelian randomization provided evidence that higher BMI partly causes depression. Using a 73-variant BMI genetic risk score, a genetically determined one standard deviation (1 SD) higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals [odds ratio (OR): 1.18, 95% confidence interval (CI): 1.09, 1.28, P = 0.00007) and women only (OR: 1.24, 95% CI: 1.11, 1.39, P = 0.0001). Meta-analysis with 45 591 depression cases and 97 647 controls from the Psychiatric Genomics Consortium (PGC) strengthened the statistical confidence of the findings in all individuals. Similar effect size estimates were obtained using different Mendelian randomization methods, although not all reached P < 0.05. Using a metabolically favourable adiposity genetic risk score, and meta-analysing data from the UK biobank and PGC, a genetically determined 1 SD higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals (OR: 1.26, 95% CI: 1.06, 1.50], P = 0.010), but with weaker statistical confidence. Conclusions: Higher BMI, with and without its adverse metabolic consequences, is likely to have a causal role in determining the likelihood of an individual developing depression.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.DH_/Department of Health/United Kingdom MC_PC_17228/MRC_/Medical Research Council/United Kingdom MC_QA137853/MRC_/Medical Research Council/United Kingdom 104150/Z/14/Z/WT_/Wellcome Trust/United Kingdom MR/M005070/1/MRC_/Medical Research Council/United Kingdom WT097835MF/WT_/Wellcome Trust/United Kingdompublished version, accepted version (12 month embargo), submitted versio

No Evidence of XMRV or MuLV Sequences in Prostate Cancer, Diffuse Large B-Cell Lymphoma, or the UK Blood Donor Population

Xenotropic murine leukaemia virus-related virus (XMRV) is a recently described retrovirus which has been claimed to infect humans and cause associated pathology. Initially identified in the US in patients with prostate cancer and subsequently in patients with chronic fatigue syndrome, doubt now exists that XMRV is a human pathogen. We studied the prevalence of genetic sequences of XMRV and related MuLV sequences in human prostate cancer, from B cell lymphoma patients and from UK blood donors. Nucleic acid was extracted from fresh prostate tissue biopsies, formalin-fixed paraffin-embedded (FFPE) prostate tissue and FFPE B-cell lymphoma. The presence of XMRV-specific LTR or MuLV generic gag-like sequences was investigated by nested PCR. To control for mouse DNA contamination, a PCR that detected intracisternal A-type particle (IAP) sequences was included. In addition, DNA and RNA were extracted from whole blood taken from UK blood donors and screened for XMRV sequences by real-time PCR. XMRV or MuLV-like sequences were not amplified from tissue samples. Occasionally MuLV gag and XMRV-LTR sequences were amplified from Indian prostate cancer samples, but were always detected in conjunction with contaminating murine genomic DNA. We found no evidence of XMRV or MuLV infection in the UK blood donors

Admixture has obscured signals of historical hard sweeps in humans (advance online)

The role of natural selection in shaping biological diversity is an area of intense interest in modern biology. To date, studies of positive selection have primarily relied on genomic datasets from contemporary populations, which are susceptible to confounding factors associated with complex and often unknown aspects of population history. In particular, admixture between diverged populations can distort or hide prior selection events in modern genomes, though this process is not explicitly accounted for in most selection studies despite its apparent ubiquity in humans and other species. Through analyses of ancient and modern human genomes, we show that previously reported Holocene-era admixture has masked more than 50 historic hard sweeps in modern European genomes. Our results imply that this canonical mode of selection has probably b een underappreciated in the evolutionary history of humans and suggest that our current understanding of the tempo and mode of selection in natural populations may be inaccurat

Gene-obesogenic environment interactions in the UK Biobank study

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND: Previous studies have suggested that modern obesogenic environments accentuate the genetic risk of obesity. However, these studies have proven controversial as to which, if any, measures of the environment accentuate genetic susceptibility to high body mass index (BMI). METHODS: We used up to 120 000 adults from the UK Biobank study to test the hypothesis that high-risk obesogenic environments and behaviours accentuate genetic susceptibility to obesity. We used BMI as the outcome and a 69-variant genetic risk score (GRS) for obesity and 12 measures of the obesogenic environment as exposures. These measures included Townsend deprivation index (TDI) as a measure of socio-economic position, TV watching, a 'Westernized' diet and physical activity. We performed several negative control tests, including randomly selecting groups of different average BMIs, using a simulated environment and including sun-protection use as an environment. RESULTS: We found gene-environment interactions with TDI (Pinteraction = 3 × 10(-10)), self-reported TV watching (Pinteraction = 7 × 10(-5)) and self-reported physical activity (Pinteraction = 5 × 10(-6)). Within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73 m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. The interactions were weaker, but present, with the negative controls, including sun-protection use, indicating that residual confounding is likely. CONCLUSIONS: Our findings suggest that the obesogenic environment accentuates the risk of obesity in genetically susceptible adults. Of the factors we tested, relative social deprivation best captures the aspects of the obesogenic environment responsible.J.T. is funded by a Diabetes Research and Wellness Foundation Fellowship. S.E.J. is funded by the Medical Research Council (grant: MR/M005070/1). M.A.T., M.N.W. and A.M. are supported by the Wellcome Trust Institutional Strategic Support Award (WT097835MF). A.R.W., H.Y. and T.M.F. are supported by the European Research Council grant: 323195:SZ-245 50371- GLUCOSEGENES-FP7-IDEAS-ERC. R.M.F. is a Sir Henry Dale Fellow (Wellcome Trust and Royal Society grant: 104150/Z/14/Z). R.B. is funded by the Wellcome Trust and Royal Society grant: 104150/Z/14/Z. R.M.A is supported by the Wellcome Trust Institutional Strategic Support Award (WT105618MA). Z.K. is funded by Swiss National Science Foundation (31003A-143914). The funders had no influence on study design, data collection and analysis, decision to publish or preparation of the manuscript. The data reported in this paper are available via application directly to the UK Biobank

Using genetics to understand the causal influence of higher BMI on depression

 This is the final version. Available on open access from OUP via the DOI in this record.Background: Depression is more common in obese than non-obese individuals, especially in women, but the causal relationship between obesity and depression is complex and uncertain. Previous studies have used genetic variants associated with BMI to provide evidence that higher body mass index (BMI) causes depression, but have not tested whether this relationship is driven by the metabolic consequences of BMI nor for differences between men and women. Methods: We performed a Mendelian randomization study using 48 791 individuals with depression and 291 995 controls in the UK Biobank, to test for causal effects of higher BMI on depression (defined using self-report and Hospital Episode data). We used two genetic instruments, both representing higher BMI, but one with and one without its adverse metabolic consequences, in an attempt to 'uncouple' the psychological component of obesity from the metabolic consequences. We further tested causal relationships in men and women separately, and using subsets of BMI variants from known physiological pathways. Results: Higher BMI was strongly associated with higher odds of depression, especially in women. Mendelian randomization provided evidence that higher BMI partly causes depression. Using a 73-variant BMI genetic risk score, a genetically determined one standard deviation (1 SD) higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals [odds ratio (OR): 1.18, 95% confidence interval (CI): 1.09, 1.28, P = 0.00007) and women only (OR: 1.24, 95% CI: 1.11, 1.39, P = 0.0001). Meta-analysis with 45 591 depression cases and 97 647 controls from the Psychiatric Genomics Consortium (PGC) strengthened the statistical confidence of the findings in all individuals. Similar effect size estimates were obtained using different Mendelian randomization methods, although not all reached P < 0.05. Using a metabolically favourable adiposity genetic risk score, and meta-analysing data from the UK biobank and PGC, a genetically determined 1 SD higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals (OR: 1.26, 95% CI: 1.06, 1.50], P = 0.010), but with weaker statistical confidence. Conclusions: Higher BMI, with and without its adverse metabolic consequences, is likely to have a causal role in determining the likelihood of an individual developing depression.Diabetes Research and Wellness FoundationAustralian Research Training ProgramMedical Research CouncilWellcome TrustEuropean Research CouncilRoyal SocietyGillings Family FoundationDiabetes UKNational Institute for Health Research (NIHR