Participants reported the coherent motion direction while high-density EEG was recorded. Using a data-driven approach, we identified two stimulus-locked EEG components with distinct topographies: an early component with an occipital topography likely reflecting sensory encoding and a later, sustained positive component over centro-parietal electrodes that we attribute to decision-related processes. The component waveforms showed clear age-related differences. In the early, occipital component, all groups showed a negativity peaking at ˜300 ms, like the previously reported coherent-motion N2. However, the children, unlike adults, showed an additional positive peak at ˜200 ms, suggesting differential stimulus encoding. The later positivity in the centro-parietal component rose more steeply for adults than for the youngest children, likely reflecting age-related speeding of decision-making. We conclude that children’s protracted development of coherent motion sensitivity is associated with maturation of both early sensory and later decision-related processes.Motion sensitivity increases during childhood, but little is known about the neural correlates. Most studies investigating children’s evoked responses have not dissociated direction-specific and non-direction-specific responses. To isolate direction-specific responses, we presented coherently moving dot stimuli preceded by incoherent motion, to 6- to 7-year-olds ( The key distinction here is that motion-specific mechanisms capture information about motion direction rather than merely reflecting dynamic changes in luminance . The N2 But how do these distinct motion-evoked responses develop? When studying infants, it is difficult to obtain the large number of trials typically used for averaging when measuring adult evoked responses. The steady-state visual evoked potential technique offers increased signal-to-noise ratio , allowinn = 33) compared to adults. While these results suggest that motion-specific evoked responses are still developing through early childhood, behavioural improvements in coherent motion processing continue past 8 years of age : 6- to 7-year-olds , 8- to 10-year-olds  and 10- to 12-year-olds .Participants were 102 children aged between 6 and 12 years and 20 adults aged between 18 and 35 years , with no reported history of developmental conditions and normal or corrected-to-normal vision (assessed with a Snellen chart). Children were recruited primarily through local schools and invitations to previously participating families, and adult participants were recruited through the University’s research participation scheme. The children were divided into three equally-sized age groups  using MATLAB  and the Psychophysics Toolbox . EEG sig2.32) were randomly positioned within a central square region (10° × 10°) on a black screen (luminance 0.22 cd/m2) and moved at a speed of 6˚/s. The dots had a limited lifetime of 200 ms (with randomised starting lifetimes), and dots moving outside the square stimulus region were wrapped around to the opposite side. A central red fixation square (0.24° × 0.24°) was present on the screen throughout the trial. Each trial consisted of a fixation period, a boil period, a stimulus period, and an offset period  in which they were asked to report the direction of ‘fireflies’ (white stimulus dots) using one of two keys  on a response box, as quickly and accurately as possible, in line with previous studies of perceptual decision-making e.g, . Block 1The experimental phase consisted of 324 trials across blocks 2 to 10. These trials included 36 repetitions of each of four coherence levels , for each coherent direction . We also included 18 catch trials with 100% coherent motion for each coherent direction. No trial-by-trial feedback was given in the experimental phase, apart from a ‘timeout’ warning if no response was provided within 2500 ms. Participants took a short break at the end of each block and were given points for the preceding block of trials. The points reflected both accuracy and response time . Trials were presented automatically in block randomised order, although the experimenter had controls to pause and resume trial presentation. Two 6-year-old participants did not complete all of the blocks in the experimental phase, instead completing 180 and 216 trials, respectively.2.5https://osf.io/fkjt6/.The procedure was approved by the local research ethics committee board. Adult participants and parents of child participants provided written informed consent, and children provided verbal or written assent. Participants first completed a Snellen acuity test to confirm normal or corrected-to-normal vision. The sensor net was then placed on the head, and adjustments were made to ensure electrode impedances were below 50 kΩ. EEG data were acquired at a sampling rate of 500 Hz with a vertex reference electrode. Participants then sat 80 cm away from the computer screen in a dark room for the experimental task. Children were closely monitored by a researcher sitting beside them. The researcher provided general encouragement and task reminders, pausing before the start of a trial where necessary . Children had short breaks at the end of each block, and a longer break halfway through , at which point the EEG recording was paused, electrode impedances were re-assessed and adjustments were made if required to bring electrode impedances below 50 kΩ. Note that for two children, impedance checks were completed instead at the end of block 4 or block 6. Children marked their progress through the blocks using a stamper on a record card. The whole session took no longer than 1.5 h. Adult participants were paid £15 and children were given a £5 gift voucher to thank them for their time. The experimental code and analysis code can be found at 2.6M = 1.69% electrodes replaced per participant; range = 0–5.08%). For most electrodes, 6 neighbours were used, whereas electrodes on the perimeter of the net were replaced with the average of the 4 nearest neighbours. We then regressed out horizontal and vertical electrooculogram (EOG) from each electrode. The horizontal EOG was calculated as the difference between the electrodes in the right and left outer canthi (electrodes 125 and 128) and the vertical EOG was determined as the difference between the sum of electrodes positioned above the eyes  and the sum of those placed on the cheek . We then replotted amplitude histograms for each individual and recalculated the 97.5th percentile, removing electrodes for each trial in which 15% of samples or more exceeded this cut-off . Next we removed transients – defined as samples that were four or more standard deviations away from the electrode’s mean – and replaced these with missing values. The data were converted to the average reference and baselined to the average of the last 100 ms of the boil period. In trials where data were removed from 19 or more (≥ 15%) electrodes, we excluded the EEG data completely . We also removed the data from two electrodes which showed unusually flat activity during the whole recording for a participant in the 6- to 7-year-old group.EEG signals were band-pass filtered offline between 0.3 and 40 Hz using NetStation’s filters, before being exported as a binary file for further pre-processing using MATLAB. We applied an 8th-order, zero-phase Butterworth highpass (0.3 Hz) filter as low frequencies were not being sufficiently attenuated by the NetStation filter. The data were epoched into trials, defined from the onset of the fixation period to the end of the offset period, and the data from each trial were median-corrected for DC offsets. Next we identified bad electrodes across each half of the session (before and after the break when impedances were re-checked). We plotted a histogram of the absolute amplitude of each participant’s data across samples and electrodes and identified outliers as those exceeding the 97.5th percentile. We removed electrodes from a half-session if they contained 15% or more samples exceeding the 97.5th percentile and replaced them with an average of the nearest neighbouring electrodes . We therefore focused on these components for our analysis. The topography of component 1 was similar for children and adults, while components 2 and 3 exhibited some differences across the age groups . To conduct age-related comparisons, we projected the child data through the component 1 and component 2 weights derived for the adults and averaged these to provide component waveforms. This approach allowed us to directly compare the response dynamics for each component across the age groups, and characterise how ‘adult-like’ the responses in the child groups were. To more extensively characterise the temporal evolution of the components, we projected a longer record of stimulus-locked data through the weights, spanning 100 ms preceding the stimulus onset to 800 ms after the stimulus onset. For some fast responses, the trial had already ended before the end of this time window, in which case these samples were left missing so that only data recorded during the coherent motion or stimulus offset period were included in the analysis.F values expected under the null hypothesis  bootstrapped F values for each factor/interaction and used the maximum sum across clusters to derive a temporal cluster threshold for each factor/interaction with an alpha level of 0.05. Finally, we computed sums of temporal clusters of significant F values in the original, non-bootstrapped data and identified clusters that exceeded the cluster threshold. To test for group differences that vary with the level of coherence, we conducted one-way between-participant ANOVAs for each coherence condition using the same bootstrapping and clustering approach. We then further investigated these group effects using bootstrapped t-tests with clustering to test for differences between each of the child groups and the adult group.We assessed the effects of group and coherence on the reliable component waveforms with a mass univariate approach, using the second-level analysis functions from the LIMO EEG toolbox . This apTo compare the results from our reliable components analysis with previous research, we also conducted a more traditional coherence-onset visual evoked potential analysis restricted to occipital electrodes. 33.1F = 11.71, p <  .001, ηp2 = .23, with planned contrasts showing that 6- to 7-year-olds (M = .72) and 8- to 10-year-olds (M = .76) made significantly less accurate responses than adults , whereas the older, 10- to 12-year-old children (M = .83) differed only marginally significantly from the adults (p =  .051). The coherence condition also affected accuracy, Fp <  .001, ηp2 = .74, with less accurate responses in the 10% (M = .62) and 30% (M = .83) conditions than in the 75% coherence condition , but no significant difference between the 50% (M = .88) and 75% coherence condition (p = .145). The interaction between coherence and age-group was not significant, F = .94, p = .47, ηp2 = .02.As expected, there were effects of age-group and coherence condition, on both accuracy and response time . There wF = 25.50, p < .001, ηp2 = .39, and coherence, F = 521.58, p < .001, ηp2 = .82. All groups of children made significantly slower responses than the adults . The highest coherence condition (M = .79 s) produced faster responses than the lower coherence conditions . Additionally, there was a significant interaction between coherence and group, F = 20.79, p <  .001, ηp2 = .35. Follow-up one-way ANOVAs for each coherence condition showed that the effect of group on response time was not significant at the lowest (10%) coherence level, F = 1.43, p =  .24, ηp2 = .04, but became more pronounced at higher coherence levels  = 27.19, p <  .001, ηp2 = .41; 50%: F = 37.11, p <  .001, ηp2 = .49; 75%: F = 36.37, p <  .001, ηp2 = .48).Mean response time also showed significant effects of group, 3.2Topographic visualisations of the forward-model projections of the two most reliable components computed from the adult data using RCA are shown in To assess the effects of group for each coherence condition separately, we conducted one-way bootstrapped, cluster-corrected ANOVAs. These analyses revealed that there were significant cluster-level effects of group for all coherence conditions . The sigF = 8.90, p < .001, ηp2 = .07, with shallower slopes in the 10% coherence condition  than in the higher coherence conditions . There was also a significant main effect of group, F = 4.22, p =  .007, ηp2 = .10, with the youngest, 6- to 7-year-old children having shallower slopes  than the adults , p =  .014, while the slopes of the older children did not differ significantly from those of adults . The interaction between group and coherence level was non-significant, F = .90, p =  .50, ηp2 = .02.Inspection of The component explaining the second largest portion of reliability (component 2) exhibited maxima over right occipital electrodes ; Fig. 3.3.3Component 2 appears to resemble the previously reported coherence-onset visual evoked potential in adults, in three ways. First, in adults, the onset of coherent motion reportedly gives rise to a negative, N2 peak at about 300 ms in occipital electrodes, in the absence of a preceding positive, P1 peak , similarr = .68, p <  .001) although the amplitude of component 2 is larger. This amplitude difference may be attributable to the fact that the reliable components waveform is a weighted average of all electrodes, in which sensors other than the occipital electrodes are weighted highly in order to maximise trial-by-trial reliability, and because this averaging process increases signal-to-noise ratio.In 3.4So far, we have shown two EEG components that change with age, and we have shown concomitant age-related changes in behaviour. Inspection of the components suggests that component 1 has the hallmarks of a decision variable  were significantly less accurate than adults, and all child groups including the 10- to 12-year-olds made significantly slower responses than adults. We identified two stimulus-locked reliable components that differed across age-groups: an early component over occipital electrodes (explaining the second-largest portion of the reliability) and a later, sustained component over centro-parietal electrodes (explaining the largest portion of the reliability).We will begin by discussing the component that explained the second-largest portion of the reliability, which resembled previously reported coherent-motion onset visual evoked potentials in adults, in both its occipital topography and time-course. This component scaled with coherence, but did not predict response times independently of coherence, suggesting that it reflects early sensory encoding preceding the decision-making process. In adults, this component had a negative peak at ˜300 ms, like the coherence-onset N2 . The ampThe component that explained the most reliability was maximal over centro-parietal electrodes and exhibited a sustained, rising positivity. A rising positivity has been previously reported with coherent-onset visual evoked potentials in adults, and has been hypothesised to reflect the decision-making process . Our comincreased with age. We therefore conclude that age-related differences in these components cannot be explained by developmental changes in skull conductivity, and instead reflect developmental changes in neural activity.It is important to consider whether the age-related changes we observe in these reliable components could arise merely as a result of developmental changes in skull conductivity with age (In this study, we used a dimension-reduction technique, RCA, which has not been used previously to investigate development over childhood. RCA identifies activity that is reliably evoked across trials, which has some similarities to computing average waveforms over a subset of electrodes, but offers three potential benefits over the more traditional analysis approach. First, components are identified in a data-driven way, rather than the researcher selecting electrodes to focus on, either post-hoc or based on previous literature. Second, RCA makes use of all electrodes, thus maximising the signal-to-noise ratio – a particular advantage when analysing high-density recordings. Third, RCA can identify multiple biologically plausible components that likely correspond to distinct neural sources. To compare the waveforms produced by RCA, we used a mass univariate statistical analysis to investigate effects at all timepoints along the component waveforms, which avoided the need to focus only on mean or peak amplitudes within defined windows . Our stuOur results show that developments in coherent motion performance through childhood are accompanied by changes in neural activity, and that age-related changes continue at least until 10–12 years of age. In a previous behavioural study, we reported that age-related changes in motion coherence sensitivity were driven by improvements in the ability to average motion information, rather than age-related reductions in local, internal noise . The curTo conclude, our results show clear age-related changes in two reliable components locked to the onset of coherent motion in children aged 6–12 years and adults. Elevated motion coherence thresholds have been reported in a range of neurodevelopmental conditions, such as autism , dyslexi


Methotrexate (MTX) is a folate analogue antimetabolite widely used for the treatment of rheumatoid arthritis and cancer. A number of studies have shown that MTX delivered via nanoparticle carriers is more potent against cancer cells than free MTX, a phenomenon attributed to higher cellular uptake of the particles compared to the saturable folate receptor pathway. In this study, a cell-based global metabolic profiling approach was applied to study the effects of MTX in both free drug form and when encapsulated in -poly(lactide-co-glycolide) (PLGA) nanoparticles on a cancer cell line, A549, and also on human-like THP-1 macrophages. The results showed that MTX loaded nanoparticles had less impact on the macrophages than free MTX, and the effects on macrophages were limited to changes in nucleotide metabolism and suppression of the tricarboxylic acid cycle, whereas free MTX also led to a drop in glycolytic activity and impairment in redox homeostasis. In contrast, MTX loaded nanoparticles showed a greater impact on A549 cells than the free drug, which was in accord with studies in other cell lines in prior literature with MTX-carrier nanoparticles. The mechanism of action is not completely understood for MTX yet, despite the drug first being introduced ~70 years ago as a cancer treatment . MTX is Malignant cells are highly proliferative, and DNA replication is essential for their survival, therefore these cells express high de novo nucleotide synthesis . MTX is The mechanism by which MTX enters cells affects its activity, kinetics and fate. MTX is structurally similar to folic acid, it binds to folate receptors and is known to enter cells through active carrier-mediated transport (reduced folate carrier RFC) which makes it readily available in the cytoplasm . Once MTMetabolomics methods potentially allow all the end products of every cellular process to be measured, and any alterations in metabolite levels might act as signals which can describe the effects of certain stimuli on cells very comprehensively . We ther22.1l-glutamine, Phorbol 12-myristate 13-acetate (PMA) and formaldehyde were purchased from Sigma-Aldrich. Ammonium carbonate was purchased from Fluka. Isopropanol and acetonitrile were LC-MS grade and dichloromethane, methanol, diethyl ether and acetone were HPLC grade provided by Fisher Scientific. Fisher Scientific provided Trypan blue 0.4% and all HPLC/LC-MS grade solvents.RPMI 1640 medium, heat-inactivated FBS, penicillin, streptomycin, l-glutamine, 100 μg/mL streptomycin and 100 U/mL penicillin, in a humidified atmosphere containing 5% CO2. The cells were used at passage number between 5 and 15.THP-1  and A549  cells were cultured in RPMI and DMEM media respectively, supplemented with 10% FBS, 2 mM 2.2−1. The solution was stirred for 10 min at room temperature and NPs were purified by dialysis overnight against 1 L of Milli-Q water using a cellulose dialysis membrane  to remove the DMSO. These NPs contained only fluorescent label and no drug and hence were termed ‘blank NPs’. Drug loaded NPs were prepared by a similar procedure, in which 2 mg MTX and 25 mg polymer were dissolved in 10 mL DMSO. Following precipitation in Milli-Q water and dialysis using the same procedure as for the blank NPs, the polymer NP suspensions were filtered through a membrane syringe filter (pore size: 0.22 μm)  before further characterisation. Determination of drug contents and encapsulation efficiencies were performed by dissolving a known amount (5 mg) of freeze dried MTX-loaded NPs in DMSO. The quantification of MTX was evaluated using UV–Vis spectrophotometry (monitoring at λmax = 304 nm). The amount of loaded drug was calculated using a standard curve of MTX in DMSO. Drug content (wt%) and encapsulation efficiency (wt%) were calculated according to the following equations:Nanoparticles were prepared using Fluorescein amine labelled PLGA to follow the cellular uptake. The fluorescently labelled PLGA was synthesised by using a fluorophore with an active nucleophilic moiety  as initiator in a solvent-free ring opening polymerisation (ROP) reaction of lactide (LA) and glycolide (GA) monomers according to a previously described method . NanoparThe in vitro drug release studies of MTX-loaded NPs were carried out in PBS, pH 7.4, whereas 5 mg of freeze dried MTX-NPs were re-dispersed in 2 mL PBS (pH 7.4) and the solution was placed in a dialysis device . The NP suspensions were dialysed against 45 mL of release media  at 37 °C and samples (1 mL) were taken at appropriate time points and replaced with 1 mL fresh medium. The collected samples were freeze dried and dissolved in DMSO. The amount of MTX was calculated using UV–Vis spectrophotometry (λmax = 304 nm) via a standard calibration curve of MTX in DMSO, 2.33 cell/cm2 and 25 × 103 cell/cm2 respectively. After 24 h, the cells were washed with PBS and treated with Blank NPs, MTX loaded PLGA NPs, and free MTX for 24 h. Thereafter, the cells were washed with PBS and the culture media were replaced with fresh fully supplemented media containing 10% AlamarBlue® reagent for 4 h. Finally, 100 μL were taken from each well and transferred to 96 black well plates and the fluorescence was measured on a TECAN plate reader at excitation/emission of 540/580 nm. Six replicates from each condition were prepared and analysed. The results are plotted as mean % viability vs control ± standard error of the mean (SEM).In 250 µL of fully supplemented medium, THP-1 and A549 cells were seeded on 48-well plates at densities of 250 × 102.45 cell/cm2 in 300 µL of fully supplemented medium. After 24 h, the cells were treated with MTX loaded PLGA NPs for 3 and 24 h. Then, the cells were washed with PBS three times, fixed with 4% paraformaldehyde for 15 min, stained with Hoechst (20 µM in PBS for 10 min) for the nucleus, stained with phalloidin Alexafluor 647 for cytoskeleton and covered with mounting medium.For confocal microscopy, cells were seeded into 8-well chamber slides (obtained from Ibidi) at a seeding density of 3 × 102.52.5.15 cell/cm2 and 25 × 103 cell/cm2 fully supplemented RPMI 1640 media and DMEM media respectively containing 50 ng/ml PMA. After 24 h, the cells were treated with free MTX, MTX loaded PLGA NPs and blank PLGA NPs for 24 h. Then, after removing the medium, the cells were washed briefly once with pre-warmed PBS at 37 °C. The cellular metabolism was rapidly-quenched and the metabolites were extracted simultaneously by adding 0.5 mL of methanol (−48 °C), and cell handling after quenching was performed on ice. The cells were then scraped and transferred to precooled fresh tubes at 4 °C. Cell solution was vortexed vigorously for 1 h at 4 °C and centrifuged at 17000 × g for 10 min at 4 °C. After the centrifugation, the supernatant was removed and dried under vacuum at room temperature. The metabolite extract was reconstituted using 70 µL of pre-cooled methanol (4 °C). 10 µL aliquots were taken from each sample to make a pooled QC in order to assess instrument performance (THP-1 and A549 cells were seeded on T25 flasks (6 replicates for each condition) at a density of 2 × 10formance .2.5.2−1 and the injection volume was 10 μL. Samples were maintained at 4 °C, and the column was maintained at 45 °C.LC-MS analysis and data processing was performed according to a previously described procedure . Brieflym/z 70–1400. The capillary and probe temperatures were maintained at 275 and 150 °C, respectively. The instrument calibration was performed by modified Thermo calibration mixture masses with inclusion of C2H6NO2 (m/z 76.0393) for positive ion electrospray ionisation and C3H5O3 (m/z 89.0244) for negative ion electrospray ionisation in order to extend the calibration mass range to small metabolites.MS was performed on an Orbitrap Exactive MS  with ESI running in positive and negative ionisation modes. Spectra were acquired in full MS scan in the range of 2.5.2.1Raw LC-MS data from the control group (untreated cells), the treatment groups , and reagent blanks were acquired using Xcalibur v2.1 software , and processed with XCMS for untargeted peak-picking . Peak ma3Initial experiments demonstrated that the prepared NPs were well tolerated by both cells lines , as evidMetabolic alterations were assessed primarily by OPLS-DA where a clear separation between the tested groups was observed, and the two cell lines showed different responses to free MTX, MTX loaded NPs and unloaded ‘blank’ PLGA NPs. The OPLS-DA plot for THP-1 cells A shows tAs expected, the proliferative A549 cells were more sensitive to MTX, where MTX treated groups were not separable see B, despitcis-aconitate) and in glycolytic activities , as shown in Two-stage statistical analysis combining multivariate and univariate analysis was adopted to study further the key metabolites upon the different treatments to both cell lines. Metabolites responsible for the separation in the OPLS-DA models having VIP values greater than 1 were selected as changing metabolites. In addition, one way ANOVA was employed to enhance the statistical power of this study and the metabolites with FDR of <0.05 were further studied, In order to study this further, we evaluated the transport of MTX-loaded NPs in THP-1 cells via super-resolution confocal microscopy . Within Further effects on cell processes were apparent in terms of nucleotide metabolism, which was altered both by free MTX and MTX loaded NPs. Thymidine synthesis, which starts by reduction of uridine to deoxyuridine, followed by phosphorylation to produce dUMP and methylation by thymidylate synthase to produce thymidine monophosphate was reduced. This occurred via suppression of thymidylate synthase by MTX, and as a result, the levels of deoxyuridine and dUMP were higher in MTX and MTX loaded NPs treated groups in comparison to the control groups. Secondly, de novo purine synthesis was also suppressed by both treatments, in particular, the third step where the enzyme phosphoribosylglycinamide formyltransferase (GART) catalyses the formation of 5′-phosphoribosyl-N-formylglycinamide (FGAR) from 5′-phosphoribosyl-glycinamide (GAR) resulting in accumulation of GAR, see .Fig. 3SuAs a comparator, the A549 lung cancer cells showed early signs of apoptosis upon treatment with free MTX and MTX loaded NPs. The levels of choline, phosphocholine, and glutathione were lower compared to the untreated groups. Similar changes have been reported in apoptotic cells , accompa4In this study, THP-1 derived macrophages and A549 lung carcinoma cells were exposed to subtoxic concentrations of free MTX and MTX loaded NPs, and global metabolic profiling was performed. The results showed that free MTX had higher impacts on the metabolome than MTX-loaded NPs in THP-1 derived macrophages. In these cells, MTX induced oxidative stress, a drop in glycolytic activity, reduction in the TCA cycle, and inhibition of nucleotide metabolism. In contrast, the effects of MTX-loaded NPs were limited to alteration of nucleotide metabolism and inhibition of TCA cycle. In comparison, A549 cancer cells were more susceptible to MTX-loaded NPs than to the free drug, in terms of purine synthesis suppression, which we suggest was linked to the different entry mechanisms for nanoparticles compared to free MTX, and lack of efflux pumps for the NPs. We believe the presented work shows that metabolomics is a valuable analytical technique that can be used to understand mechanisms of action of drugs and formulations in various clinically important cell lines.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Prader-Willi syndrome (PWS) is the most common genetic obesity syndrome, with associated learning difficulties, neuroendocrine deficits, and behavioural and psychiatric problems. As the life expectancy of individuals with PWS increases, there is concern that alterations in brain structure associated with the syndrome, as a direct result of absent expression of PWS genes, and its metabolic complications and hormonal deficits, might cause early onset of physiological and brain aging.In this study, a machine learning approach was used to predict brain age based on grey matter (GM) and white matter (WM) maps derived from structural neuroimaging data using T1-weighted magnetic resonance imaging (MRI) scans. Brain-predicted age difference (brain-PAD) scores, calculated as the difference between chronological age and brain-predicted age, are designed to reflect deviations from healthy brain aging, with higher brain-PAD scores indicating premature aging.n = 20; age mean 23.1 years, range 19.8–27.7; 70.0% male; body mass index (BMI) mean 30.1 kg/m2, 21.5–47.7; n = 19 paternal chromosome 15q11–13 deletion) and age- and sex-matched controls  adults (BMI PWS vs. control P = .002). Brain-PAD was significantly greater in PWS than controls . Brain-PAD remained significantly greater in PWS than controls when restricting analysis to a sub-cohort matched for BMI consisting of n = 15 with PWS with BMI range 21.5–33.7 kg/m2, and n = 29 controls with BMI 21.7–34.2 kg/m2 . In the PWS group, brain-PAD scores were not associated with intelligence quotient (IQ), use of hormonal and psychotropic medications, nor severity of repetitive or disruptive behaviours. A 24.5 year old man (BMI 36.9 kg/m2) with PWS from a SNORD116 microdeletion also had increased brain PAD of 12.87 years, compared to 0.84 ± 6.52 years in a second control adult cohort .Two separate adult cohorts underwent brain-predicted age calculation. The main cohort consisted of adults with PWS (This increase in brain-PAD in adults with PWS indicates abnormal brain structure that may reflect premature brain aging or abnormal brain development. The similar finding in a rare patient with a SNORD116 microdeletion implicates a potential causative role for this PWS region gene cluster in the structural brain abnormalities associated primarily with the syndrome and/or its complications. Further longitudinal neuroimaging studies are needed to clarify the natural history of this increase in brain age in PWS, its relationship with obesity, and whether similar findings are seen in those with PWS from maternal uniparental disomy. •Prader-Willi syndrome is the commonest genetic obesity syndrome.•19 of the 20 adults with PWS had a paternal deletion in chromosome 15q11–13.•Adults with PWS had increased brain age using structural MRI with machine learning.•This was independent of their higher BMI, and use of growth and sex hormones.•Another man with a SNORD116 microdeletion also had an increased brain age. PWS resPWS has an estimated birth incidence of 1 in 27,000 individuals and presents with a characteristic but variable phenotype affecting many organ systems . PWS is In the past, mortality rates for people with intellectual disabilities was higher than it is now , and in People with PWS have abnormal development in certain brain areas , with soPremature aging and brain atrophy. Reports from older PWS patients describe cases of cortical atrophy and signs of premature aging, similar to that seen in other neurodegenerative diseases  gene clusters, especially the SNORD116 cluster. The phenotype of such cases includes hyperphagia, severe obesity, learning difficulties and hypogonadism .To evaluate age-related changes to brain structure, neuroimaging data has been used to predict chronological age based on a multivariate machine-learning model of healthy individuals . Using tIn this paper, we investigated brain age in young adults with PWS compared to healthy individuals, to determine whether they exhibited patterns of arrested development or premature aging. We also explored whether brain structural changes were related to common clinical characteristics of PWS, namely high body mass index (BMI), low intellectual quotient (IQ), hormonal status, psychiatric medication use, and behavioural measures, to identify possible mechanisms and associations with the abnormalities accompanying PWS. We also examined brain age in one of the rare cases of PWS due to a SNORD116 microdeletion to identify potential gene(s) in the PWS chromosomal region contributing to changes in brain age .22.12.1.1Training sets are typically used in machine learning to discover relationship patterns that can be applied for future predictions without explicit input by the researcher. In this case, T1-weighted MRI scans were collected from public sources (Supplementary Table 1), and included 2001 typically developing individuals , age mean ± SD 37.0 ± 18.1 years, range 18.0–90.0). The participants were screened according to their respective study's criteria, to ensure the absence of psychological or neurological diagnosis, and major health conditions. The scans were used to derive a statistical model of healthy brain structure across the lifespan.2.1.2The model constructed by the training set was then applied for predictive analysis on the test set. In our study, the test sets comprised two separate adult cohorts.2.1.2.1n = 20 adults with PWS (mean ± SD age 23.1 ± 2.4 years) and n = 40 age- and sex-matched, typically developing controls (age 22.9 ± 2.2 years) (n = 36) were recruited and tested as part of the NeuroScience in Psychiatry Network (NSPN) U-Change project, enabling the selection of age and sex-matched controls. Additional control participants aged 24–28 years (n = 4) were recruited from the local population.This cohort included 2 years) . All par2 years) . TypicalFor the PWS group, where genetic diagnosis of subtypes was not available in medical records, the genotype was confirmed using blood or saliva samples. Records of medication and hormonal therapies were given by participants, parents and care-givers. IQ was assessed in the PWS group using the Wechsler Adult Intelligence Scale Fourth Edition . For the control group, a shortened version of the WAIS was used (vocabulary and matrix reasoning subtests) from the Wechsler Abbreviated Scale of Intelligence Second Edition . The Developmental Behaviour Checklist for Adults (DBC-A) was used to assess behaviour difficulties and temper outbursts in PWS using DBC-A disruptive score . The RepExclusion criteria were: inability to tolerate MRI scans, history of psychiatric disorder for controls or current psychiatric disorder disrupting compliance with study demands for PWS participants, history of neurological disease or trauma, currently or recently participating in a clinical trial for medicinal investigation, metal object precluding MRI, and currently being treated for drug or alcohol dependency. Ethical approval for the study was granted by the Cambridge East (East of England) NHS National Research Ethics Committee (REC number 13/EE/0373).2.1.2.22 , unstable depression (dose change of anti-depressants in last 3 months). Subjects on anti-hypertensive, lipid lowering medication or anti-depressant medication were not excluded.A total of MI ≥30.0 . All con2.22.2.1For the training set, the acquisition of three-dimensional T1-weighted MRI scans was conducted using various parameters and field strengths (either 1.5T or 3T) depending on local study protocols (Supplementary Table 1).2.2.1.1Three-dimensional, high-resolution, T1-weighted MRI scans for the acquisition of structural images for the PWS and control groups, used a Siemens 3T scanner (Siemens MAGNETON 3T Trio), at the Wolfson Brain Imaging Centre, University of Cambridge, Addenbrooke's Hospital, UK, using a 32-channel head coil, with the following acquisition parameters: echo time (TE) 2.20 ms, repetition time (TR) 18.70 ms, flip angle 20°, field-of-view 256 mm, voxel dimensions 1.0 × 1.0 × 1.0 mm.2.2.1.2Three-dimensional, high-resolution, T1-weighted turbo field echo brain scans collected from 3T Philips Achieva scanner, Robert Steiner MRI Unit, MRC Clinical Sciences Centre, Imperial College London, UK, using an 8-channel head coil with the following acquisition parameters: echo time (TE) 4.6 ms, repetition time (TR) 9.7 ms, flip angle 8°, field-of-view 240 mm, voxel dimensions 0.94 × 0.94 × 1.2 mm.2.2.2Two independent neuroradiologists reviewed all of the T1 isotropic images in cohort 1 from 20 patients with PWS and 40 controls . The patient with a SNORD116 microdeletion additionally had T2, FLAIR and dedicated MRI pituitary images (without gadolinium contrast) reviewed. Both were blinded to the identity of the patients. All abnormalities were documented, including previously described intracranial defects in PWS such as ventriculomegaly, peri-sylvian polymicrogyria, abnormal insula closure and parieto-occipital abnormalities .n = 95 controls in cohort 2 (for the SNORD116 microdeletion patient) had previously been reported by a Consultant Neuroradiologist as having no clinically significant abnormalities as part of routine clinical governance for our neuroimaging research studies.The T1 MRI brain scans of the 2.2.3An overview of the methods is represented in Acquired MRI data were preprocessed using the Statistical Parametric Mapping (SPM12) software . The T1-weighted images underwent bias correction before being segmented into grey matter (GM), white matter (WM) and cerebral spinal fluid (CSF). Intracranial volume (ICV) was calculated as the sum of GM, WM and CSF. The segmentation process was followed by a quality check to verify the precision of the segmentation. At this stage, images from three PWS participants had to be re-segmented, as they failed the initial SPM segmentation. This was done using the FMRIB Software Library Brain Extraction Tool (FSL BET) to remove extraneous non-brain tissue from the field-of-view, after manual realignment of the head images and resetting of the origin to the anterior commissure in SPM. Preprocessing of the T1-weighted images ensured alignment and corresponding voxel-wise analysis when running the machine learning analysis. The resulting 3D maps were registered to a group-averaged template in Montreal Neurological Institute (MNI152) using the nonlinear SPM-DARTEL algorithm and smoothed with a 4 mm kernel.n = 2001)  . For eac = 2001) .To validate the age prediction model, a 10-fold cross-validation was applied to the training set, whereby the data is randomly split into 90% for training the model and 10% for testing the model. This procedure was repeated so that all data served as both training and testing. The accuracy of the model was calculated as the correlation between the real age and the predicted age, and the statistical significance of the model was computed using a permutation test (x1000).Once validated, the model was applied on the two testing populations , 3D maps of GM and WM, to estimate the age of the participants based on their brain structure. Brain predicted-age difference (brain-PAD) scores were then calculated for each participant by subtracting the real age from the predicted age. Accordingly, a positive brain-PAD value would indicate an older-appearing brain.2.3www.R-project.org) and SPSS v24, to assess normality using Kolmogorov–Smirnov test, relationships between variables and compare experimental groups. For brain-age prediction, brain-PAD scores were set as the dependent variable, and co-variates for linear regression analysis included BMI, IQ, WM and GM volumes, ICV, or behavioural measures, to study the effect of a variable on brain-PAD within groups and between groups (PWS and controls), reporting Pearson's correlation coefficients. Brain-PAD scores were also regressed against chronological real age. Comparison between PWS and controls, and within groups between sexes and medication or hormone use, used two-way unpaired t-test, or Mann-Whitney U test for data that was not normally distributed.Statistical analysis was conducted using R v3.3.3 (33.13.1.1P = .73 and P = .70 respectively). The mean IQ was significantly lower in the PWS group , and the mean BMI was significantly higher in the PWS group . Of the 20 participants with PWS, 17 had been treated with growth hormone (7 of whom were still on GH at the time of the MRI scan), and 15 had been treated with sex hormones (7 of whom were still receiving these). One of the PWS subjects had maternal UPD, while the remaining 19 had a paternal chromosome 15q11–13 deletion.Demographics of the PWS and matched control cohort are given in 3.1.2Demographics of the man with SNORD116 microdeletion and his control comparison group are given in 3.2In cohort 1, one out of the 20 PWS participants had dilated ventricles and widened sulci, and one arrested hydrocephalus with possible aqueduct stenosis , but none of these abnormalities were seen in the controls . Peri-syThe man with a SNORD116 microdeletion had no visible abnormality on T1-, T2-weighted or FLAIR MRI brain scans or dedicated pituitary MRI scans.3.3P < .01) between brain-predicted age and chronological age and explained 88% of the variance (R2). The mean absolute error (MAE) and root mean squared error (RMSE) of prediction were respectively 5.01 and 6.31 years. This stage validated our machine learning model as a predictor of age based on neuroimaging data for use on our test set consisting of controls and PWS participants.The chronological age of the training set was accurately predicted by the machine learning model using the 3D T1-weighted MRI scans . The ten3.4t = 3.29, P = .002 (Participants with PWS showed significantly higher brain-PAD scores (mean ± SD +8.74 ± 9.14 years) than the control group (+1.50 ± 7.42 years), with overall effect size mean ± SEM 7.24 ± 2.20 years , P = .002 .Fig. 2Brt = 2.50, P = .015), so these potential outliers were not driving the main group effect.Two participants with PWS showed very high brain-PAD scores (> + 25 years). However, brain-PAD scores remained greater in PWS than controls when excluding these subjects .There was no significant difference in ICV or other tissue volumes between PWS and control groups , and no P = .073, P = .003, adjusted r2 = 0.25). Adjusting for GM volume, brain-PAD remained significantly greater in PWS than controls   A.Fig. 3BP = .32, P = .006, adjusted r2 = 0.24). Adjusting for WM volume, brain-PAD remained significantly greater in PWS than controls   B.3.6P = .54) . When combining the groups, adjusting for group differences, there was a trend for an overall positive correlation between brain-PAD and BMI . However, when adjusting for differences in BMI between groups, by including BMI in the model, brain-PAD remained significantly greater in the PWS compared to control group   C. There  = .048) C.t = 2.15 P = .037), when matching the groups for BMI, by including only those subjects with PWS having BMI <34.2 kg/m2, the maximum BMI in the control group , and controls with BMI >21.5 kg/m2, the minimum BMI in the PWS group .Furthermore, brain-PAD also remained significantly greater in the PWS compared to control group  , or when combining groups with adjustment for group differences   D. There  = 0.13) D.t = −1.54, P = .14; controls: male effect size 3.11 ± 2.44 years , t = 1.28, P = .21), or in the whole cohort, adjusting for group differences .There was no significant sex difference in brain-PAD in either group , or disruptive behaviour using DPC-A disruptive score .In the PWS group, there was no significant correlation between brain-PAD and either repetitive behaviour using RBS-R sameness score  or sex hormones , or current use of any psychoactive medication , anti-psychotics  or anti-depressants .In the PWS group, there was also no significant effect on brain-PAD of medication use, for growth hormone , which means that most of PWS individuals in our study showed an increased predicted brain age. However, any variability in brain-PAD scores in the PWS group could not be explained by medication use, including growth or sex hormones or psychotropic drugs, sex or current BMI. In addition to normal individual variation, potential factors might be duration or timing of hormonal replacement, duration or dosage of psychotropic medication, age of onset and duration of obesity, or metabolic complications of obesity. Greater brain age has been associated with poor health outcomes, so the variability might become more relevant with longitudinal follow-up.Participants with PWS showed signs of premature brain aging and early onset atrophy rather than a fundamental arrest of brain development. Patterns of brain structure more closely resembled healthy older brains rather than healthy younger brains, which might be indicative of premature neuronal loss and atrophy. Even slight decrease in brain tissue volumes might be picked up by the trained model as a biomarker of aging as it has already been established that there is a widespread reduction of GM and WM volumes from young adulthood and onwards .Previous studies using food intake or food cue related paradigms have revealed functional neuroimaging abnormalities in PWS, focusing mainly on subcortical and cortical structures usually involved in eating behaviour, reward and motivation . HoweverInterestingly, the higher brain age in PWS was not related to changes in GM and WM volumes which did not differ between controls and PWS groups, emphasizing the strengths of brain age over standard structural volumetrics, as the patterns picked up by the model are spatially distributed. Indeed, more localised examination of structural brain differences in PWS using voxel-based morphometry from the same cohort as in this study, has revealed increased volume in the prefrontal cortex, especially medially, the majority of the cingulate cortices, insula cortices, and areas of the parietal and temporal cortices, caudate, putamen and thalamus, with increased cortical volume largely driven by greater cortical thickness. Reduced volume was found in ventromedial prefrontal areas, medial temporal lobe, bilateral temporal poles, and right lateral prefrontal cortex, while myelination of the cortex was broadly unchanged, with the exception of some highly localised areas, including the insula .Our current findings contrast with a previous study reporting that children with PWS showed a fundamentally arrested brain development . This waAlthough our study is not the first to examine brain structure in young adults with PWS, it is the first to use a pre-defined model of the structural pattern of the brain in healthy adults to compare structural abnormalities in PWS. Moreover, our analysis has an emphasis on markers of aging, known to be associated with a higher risk of death, poorer cognitive function and impaired physical fitness . The usen = 20), but this is usual in studies of PWS because of the rarity of the disease and the difficulty of recruitment and scanning. Scans from different sources were used for the large training set, for which detailed demographic data were not available. However, it was verified that individuals were free from neurological, psychiatric or mental diseases before using the scans to train the model, and the large number of data used (n = 2001) puts the brain-PAD scores of the PWS group in appropriate context. The limited age range of our cohort (both PWS and controls) is both a strength and a constraint, since it eliminates the confounding effect of age, but it may restrict generalizations of the results to other age groups. Therefore, there is still a possibility that brain-PAD scores in subjects with PWS under 18 years of age might be reduced.It is important to acknowledge the limitations and strengths of our techniques. First and foremost, the sample size of our PWS group is relatively small  and growth restriction . MoreoveThe complex genetic background of PWS, together with the complicated role of the genes involved in the syndrome might explain some the neurodevelopmental deficits driving our results, as well as the large variability in brain-PAD scores. The exact function and relative importance of each of the genes involved in PWS still needs to be further elucidated, although it is clear that some rare genes are key to the development of the central nervous system in early stages, or in later stages, thus promoting early atrophy.Necdin (Ndn), is a neuronal cell cycle regulator, usually upregulated during development, differentiation and migration of neuronal cells. Ndn mutant mice have abnormal neuronal migration and cortical development  cluster. Given that this patient exhibits a similar increase in brain-PAD score within the range of brain-PAD scores of our PWS cohort, we hypothesize that the loss of function for this genetic cluster might be sufficient to cause premature brain aging. Two studies on mice lacking the snord116 gene cluster showed growth and motor learning deficiencies, and a possible hyperphagic phenotype  would also help understand the role of specific genes in the PWS regions, potential mechanisms leading to premature aging in PWS, role for gene dosage outside of the PWS critical region, and the potential neurobiological basis of genotype-phenotype interactions in PWS, for example the increased risk of psychosis with mUPD. Given the widespread neurological effects of PWS, it will be especially helpful to incorporate different imaging and analysis techniques such as DTI and structural and functional MRI and multi-modality brain-age prediction in the study of older patients with PWS.In future investigations of PWS, the use of a larger age range, especially including children and adolescents, individuals with more severe obesity, and a longitudinal study would help uncover the natural history of brain development and aging and the interaction with development of obesity. Understanding how brain aging differs between the different genotypes  vs. paternal deletion, type I vs type II deletions, and rarer microdeletions involving snoRNA clusters, and specific gene mutations e.g. Further understanding of the potential role of obesity and associated metabolic factors, and PWS genotypes, in the development of increased brain aging in PWS will emphasize additional reasons to develop strategies and novel therapies to prevent and treat overeating, weight gain and metabolic syndrome, and perhaps even targeted to particular genotypes. Furthermore, as adults live longer with PWS with earlier diagnosis and improved multi-disciplinary care, including the use of specialist residential placements, the presence of accelerated brain aging identified through MRI brain scans, may identify individuals at risk of cognitive decline in later adulthood, perhaps through neurodegenerative pathological processes leading to dementia, who may need closer monitoring, aggressive treatment of reversible risk factors and perhaps early use of pharmacological therapies .In conclusion, this neuroimaging study indicates that PWS is associated with an increased brain-age from early adulthood. This conclusion challenges the competing hypotheses (arrested development vs. premature aging) trying to explain structural brain abnormalities in PWS. This approach suggests that there may be neurological implications of the syndrome in older age, which may have important clinical implications as the life expectancy of patients with PWS is prolonged with better management of the food environment and avoidance of obesity-related complications.


The current study provides an overview of quantity and quality of MRI data in a large developmental twin sample , and investigated to what extent scan quantity and quality were influenced by genetic and environmental factors. This was examined in a fixed scan protocol consisting of two functional MRI tasks, high resolution structural anatomy (3DT1) and connectivity (DTI) scans, and a resting state scan. Overall, scan quantity was high , while scan quality decreased with increasing session length. Scanner related distress was negatively associated with scan quantity , but not with scan quality . In line with previous studies, behavioral genetic analyses showed that genetics explained part of the variation in head motion, with heritability estimates of 29% for framewise displacement and 65% for absolute displacement. Additionally, our results revealed that subtle head motion (after exclusion of excessive head motion) showed lower heritability estimates (0–14%), indicating that findings of motion-corrected and quality-controlled MRI data may be less confounded by genetic factors. These findings provide insights in factors contributing to scan quality in children, an issue that is highly relevant for the field of developmental neuroscience. With thScan quality is not only influenced by head motion but can also be influenced by additional sources of noise such as scanner drift and respiratory signals . HoweverIn addition to trait-like, genetic influences on scan quality, we also investigated the influence of environmentally affected factors, such as emotional state towards the MR scan and MR protocol length. Previous research has described several child-specific scanner environment adaptations that have been used in  radiology departments . One adaSimilar to scan quantity, we investigated whether scan quality was related to emotional state, and to what extend scan quality was influenced by genetics. As an additional factor of interest, we examined scan quality across the duration of the MR session, as children tend to lose focus faster than adults, which may result in increased motion over time . Scan qu22.1Participants in this study took part in the preregistered longitudinal twin study of the Leiden Consortium on Individual Development L-CID; . The Dut2.2Participating twins visited the lab with their primary parent (defined as the parent that spends the most time with the children). Before the visit to the lab families received a step-by-step explanation of the MRI procedure, including a description of the magnetic field, the materials used during the MRI scan , and the movies that were available to watch. The step-by-step approach was specifically aimed at the young participants, and consisted of child appropriate texts and illustrative pictures. The lab visit took place at the Leiden University Medical Centre (LUMC) and consisted of four components: the MRI preparation session, the MRI scan session, parent-child interaction tasks, and a child behavioral tasks session. In the current study, data from the MRI preparation session and the MRI scan session were evaluated. During the practice session the whole family was further introduced to the aims of the study, and carefully instructed about safety around the MRI system and the influence of motion on the scans. Next, the children participated in a MRI simulation with the MRI researcher. In the MRI simulation, the exact same steps that were also explained in the step-by-step explanation were followed. A prototype of a Philips scanner (without a working magnet) was used to mimic the MRI environment. Children listened to MRI sounds via a laptop. They were shown the various materials  for the MRI procedure. Next, they were asked to practice lying very still on the scanner bed while wearing the headphones and button box. Finally, they practiced looking in the mirror on the coil, while they were slowly slid into the MRI bore. After the MRI simulation, the children were familiarized with the MRI tasks on a laptop. First-born and second-born children of each twin pair were randomly assigned to the MRI scan session or to the parent-child interaction tasks as their first activity. There were no differences in outcome measures  for children that were scanned directly after the MRI simulation or an hour later.The MRI session lasted 60 min, including two fMRI tasks, high resolution T2 and T1 scans, diffusion tensor imaging (DTI) scans and a resting state (RS) fMRI scan. The first fMRI task was the Social Network Aggression Task (SNAT), as described in detail in 2.3tensed and how excited they felt about the scan session, by pointing to the cartoon smiley that best represented their feelings . Mean framewise displacement (FD) was calculated for all runs, for all participants  maximum motion in all directions as sufficient quality  was carried out using Motion Correction FMRIB’s Linear Image Registration Tool MCFLIRT . Absoluticipants . Reliabi quality . For the quality . RS fMRI quality .Table 1Fhttp://surfer.nmr.mgh.harvard.edu/). The pre-processing pipeline included non-brain tissue removal, cortical surface reconstruction, subcortical segmentation, and cortical parcellation  = 5.07, p = .002), with post hoc analyses revealing a significant difference between scans rated as ‘Failed’ and scans rated as ‘Excellent’ to ‘Doubtful’ . Therefore, for analyses using more lenient quality control, we included data that were classified as ‘Excellent’, ‘Good’ and ‘Doubtful’.Structural T1 scans were pre-processed in FreeSurfer (v5.3.0). Anatomical labeling and tissue classification was performed on the basis of the T1- weighted MRI image using various tools of the FreeSurfer software  and in R version 3.5.0 . Scanner33.13.1.1M= 5.10, SD= .93), and after the MRI scan , compared to before the MRI simulation  = 23.25, p < .001, all Bonferroni corrected pair-wise comparisons p < .05). Furthermore, children reported significantly less tension before the MRI scan , and after the MRI scan , compared to before the MRI simulation  = 124.65, p < .001, all Bonferroni corrected pair-wise comparisons p < .05). Ratings of tension by the researchers and parents showed a similar pattern . Scanner related distress  was more strongly correlated between children and researchers , than between children and parents , but it should be noted that the child and researcher filled in the rating at the same form and therefore were not independent. The multi-informant scores  of tension and excitement were significantly negatively correlated: r=-.33, p < .001 before MRI simulation, and r=-.35, p < .001 before the MRI scan.To investigate scanner related distress preceding and following the MRI scan, we measured the emotional state towards the scanner using the visual analogue scales. Over time, children reported more excitement and less tension, see  pattern b and wer3.1.2mzr range = .24–.58; dzr range = .22–.48; all p’s<.05, see To investigate genetic and environmental influences on scanner related distress, we calculated Pearson’s within-twin correlations for MZ and DZ twins and performed behavioral genetic analyses. Within-twin correlations for the multi-informant ratings of scanner related distress  were similar for MZ and DZ twins , and participants who missed one or more scans . However, we found no effects of age (t(470)=-1.08, p = .28) or gender  = 1.86, p = .12). We also found no association between age and the number of completed scans .Of the 512 included participants, 24 children (4.7%) never started with the MRI scan due to MRI contra indications (n = 6); lack of parental consent (n = 4); technical error (n = 1), or substantial anxiety (n = 13), see Table S2. As can be seen in Table S2 and 3.2.2M=8.29, SD=2.08) showed a positive association between excitement towards the scan and the number of scans completed , and a negative association between tension towards the scan and the number of scans completed , see Pearson’s correlations on the number of completed scans  than DZ twins , Z = 3.21, p < .001. Behavioral genetic analyses revealed substantial influences of genetics  and unique environment/measurement error , with no influence of the shared environment .To investigate genetic and environmental influences on scan quantity (number of scans completed), we calculated Pearson’s within-twin correlations for MZ and DZ twins and performed behavioral genetic analyses. Fisher r-to-z transformations showed that within-twin correlations for scan quantity were significantly stronger for MZ twins .An overview of the number (and percentage) of scans with sufficient quality relative to the quantity of the scans is provided in 3.3.2p = .149), see 2χ = 11.70, p=.001), see 2χ = 8.85, p = .002), see There was an increase in head motion over time, both framewise as well as absolute  displacement . After e3.3.3M=5.58, SD=2.47,) showed no association with excitement or tension . Children’s own estimate of excitement after the MRI scan was significantly correlated to scan quality , whereas tension after the MRI scan was not related to scan quality . Pearson’s correlations of the quantitative measures of scan quality (i.e. head motion based on the fMRI runs) showed a positive correlation between excitement before the MRI scan and mean FD , a positive association between absolute displacement and excitement before the MRI simulation  and before the MRI scan ; and a negative association between absolute displacement and tension before the MRI simulation . However, these correlations did not survive Bonferroni correction (Bonferroni corrected α = .008).Pearson’s correlations on the number of included scans  than DZ twins , Z = 2.40, p = .016. Behavioral genetic analyses revealed substantial influence of genetic factors  and unique environment/measurement error , with no influence of shared environment .Within-twin correlations for general scan quality (percentage of scans included) were significantly stronger for MZ twins , see mzr =.70, p < .001) than between DZ twins , indicating substantial genetic influences. More detailed behavioral genetic analyses showed that framewise displacement was significantly influenced by genetics, with a heritability estimate of 29%  and DZ twins  and DZ twins . Behavioral genetic analyses revealed low heritability estimates for moderate head motion , and in addition showed influence of shared environment. That is to say, influence of genetics on moderate framewise displacement was estimated as 12% (95% CI: [0-51%]) and 22% of the variation was explained by shared environment (95% CI: [0–45%]). Influence of genetics on moderate absolute displacement was 14% (95% CI: [0–46]), and 15% of the variation was explained by shared environment  and DZ twins  and DZ twins . Behavioral genetic analyses revealed even lower heritability estimates for minimal head motion . Overall, scan quantity was high and 88% of the children completed all runs. We report a drop in the number of runs completed after approximately 45 min of scan time, which is comparable with prior findings in this age range . Scan qu4.1As a complement to the growing literature on familial similarities in head motion , we alsomz = 12 and Ndz = 22) and the differences in exclusion based on head motion, since Reassuringly, heritability estimates for subtle head motion (after exclusion based on excessive head motion) were considerably smaller, ranging from 0 to 14%. This is contrary to previous findings of 4.2An additional goal of this study was to examine how emotional state towards the scanner was related to scan quality and quantity. Consistent with findings for quality controlled head movement, reports of emotional states showed little to no influence of genetics, but a moderate to strong relation with shared environmental influences. These findings suggest that emotional states can be significantly influenced by preparation of the scanner experiences. It was interesting to note that children’s tension was on average rated higher by researchers and parents than by children themselves, which is in line with previous studies suggesting that children may underreport their anxiety . Multi-iOne aspect that did show influence on scan quality was the length of the MRI scan session. Results showed that a protocol of >30 min resulted in less than 50% sufficient quality on all scans in this age range of 7-9-year-olds. This is in line with other research that also recommends a scanning time of 30–40 min for young children , whereas4.3moderate  and minimal head motion  than the analyses on the full sample . As the statistical power of genetic studies is influenced by the sample size , nevertheless, several other factors can influence scan quality, amongst others: thermal noise, respiratory signals, and scanner drifts . Future ple size , differe4.4We report that participants’ scanner related distress was associated to scan quantity, but not to scan quality. Overall, scan quantity was high, as 88% of the children that started the protocol also completed it. The percentage of sufficient scans was considerably higher (49%) in the first 30 min of the protocol than in the full 60-minute protocol (20%), indicating that shorter scan protocols have less attrition. Consistent with previous studies , the beh


Net primary productivity, soil respiration and C offtake as cut herbage and via livestock grazing at 1.9–15.9, 1.74–12.5, and 0.34–11.7 t C ha−1 a−1, respectively, were the major C fluxes. No significant differences were found between the productivity of any of the swards apart from in the RS year of establishment. However, NUE of the livestock production system was significantly greater for the HSGC and HSGC-T swards at 32 and 42% compared to all other swards, associated with the low chemical-N fertiliser inputs to these clover-containing swards. Our findings demonstrate opportunities for improving NUE in grazing systems, but also the importance of setting realistic NUE targets for these systems to provide achievable goals for land-managers.There is currently much debate around the environmental implications of ruminant farming and a need for robust data on nitrogen (N) and carbon (C) fluxes from beef and sheep grazing systems. Here we use data collected from the North Wyke Farm Platform along with the SPACSYS model to examine the N and C budgets and the N use efficiency (NUE) of grassland swards at different stages of establishment. We assessed the transition from permanent pasture (PP) to a high-sugar grass (HSG), and a mixed sward of HSG with white clover (HSGC), identifying data specifically for the reseed (RS) years and the first year following RS (HSG-T and HSGC-T). Dominant fluxes for the N budget were N offtake as cut herbage and via livestock grazing, chemical-N fertiliser and N leaching at 88–280, 15–177, and 36–92 kg N ha Much of the global growth in protein production has come about through intensifying animal production, which is often dependent upon concentrate feeds sourced beyond the farm-gate. Currently one-third of arable land is used for feed production and based on current consumption patterns this area is expected to increase  as protein, particularly in animal-based products. increase . Inclusiincrease . ConsequExtensive grasslands cover an estimated 26% of the world terrestrial surface and together with permanent pastures amount to 3.5 billion ha globally – more than twice the total area of croplands . The graNutrient budgets are a valuable tool to summarise and understand nutrient cycling in agricultural systems and to assess their environmental impact. In managed grasslands, N and C exports within harvested biomass, as gaseous emissions, via hydrological losses and within the final exported product, can make significant contributions to the system N and C budget. As imbalances are not sustainable in the long term, N and C budgets can be used as indicators and regulatory policy instruments for nutrient management, which can provide insight for methods to reduce losses and increase efficiency. Few studies have attempted to calculate system level N budgets from managed grasslands , whereasTo provide complete N and C budgets based on monitoring data is generally not feasible because of the difficulty in measuring some of the components. Modelling is an effective way to account for these unmeasured budget components, if model selection is carefully made and as far as possible validated with observed values. Over the last 40 years, crop growth models have been continuously improved to dynamically simulate processes of C, N and water balance with various time-steps to predict crop growth, development and final yield . These mhttps://nwfp.rothamsted.ac.uk/; see 2 exchange (NEE) of intensively managed grasslands by offering an estimation of the C balance, above and below ground C transfer and soil C cycle on a field scale. Thus, we use both monitored and modelled data to examine the impact of the transition from permanent pasture through to the establishment of novel grassland sward-varieties on the system N and C budgets. We apply the N budget data to the 2-dimensional NUE framework presented by the Here, we use the detailed farm-reports for the beef and sheep North Wyke Farm Platform at Rothamsted Research . The site falls within the “Good” grass growth class . The NWFP has a temperate climate with average annual precipitation of 1008 mm and mean daily minimum and maximum temperatures of 6.9 and 13.8 °C, respectively (data from 2000 to 2016). The site overlays clay shales and the predominant soil type is a Stagni-vertic Cambisol , hypothesised to optimise protein utilisation and within-ruminant N partitioning ; consequently, N was provided via biological N fixation (BNF) with additional FYM amendments. Each of the three swards were replicated across 5 hydrologically-isolated subcatchments, equating to a ˜22 ha “farmlet” for each sward (We used the NWFP to examine the transition from permanent pasture (PP) to two reseeded swards in comparison with a continuing PP sward, with replication at the subcatchment scale. The different swards are described in full by itioning , and wasch sward , and a tch sward . The traEach farmlet was grazed by 30 beef cattle and 75 ewes with their lambs. Cattle were introduced to the NWFP after weaning, at 6 months of age, and remained on their respective sward treatment until removed for slaughter. Cattle were housed over winter  and fed grass silage harvested from the individual treatments. Ewes typically grazed longer into the winter season (late November to early January) and were then housed and fed off the NWFP prior to lambing; they were subsequently returned to the NWFP the following Spring  with their lambs. Average lambing rates were 1.8 for the years 2011 through to 2015, and 1.7 in 2016.st January to 31st December). The system boundaries of our approach comprise only the pasture (soil and vegetation) and the animal contribution to the budget by grazing forage and importing excreta during the grazing periods on the investigated subcatchment . All subcatchments that underwent ploughing and reseeding with a new sward were categorised as reseed  for the RS year, as the physical disruption of the reseeding process, the bare ground and sward establishment, was considered the most significant process for those subcatchments within the year. The subcatchments for the year following RS were designated transition (T) years under the new treatments, therefore n = 5 for HSG-T (HSG transition) and n = 5 for HSGC-T (HSGC transition). After the transition year the new swards were considered established and were designated as HSG and HSGC (n = 5 for each). Individual N budgets were derived for each subcatchment , fertiliserN is chemical fertiliser-N, FYMN is farmyard manure-N, excretaN is N excreta from grazing livestock including both dung and urine. fixationN is biological N fixation, leachingN is hydrological N losses, volatilisationN is ammonia losses via volatilisation, and denitrificationN is N losses via denitrification.The data were analysed at the subcatchment scale, whereby subcatchments were categorised as PP for those within the PP farmlet and for all other subcatchments prior to being reseeded (DMI) by grazing animals was estimated by:ME is the metabolisable energy content of the cut herbage (as MJ kg−1 DM); fM, aM and gM are energy requirement for livestock fasting, activity and growth, respectively, and determined by animal average weight  and liveweight gain . A full description of energy requirements can be found in the literature (Dry matter intake (terature .intakeN) by animals was divided into two parts: Nitrogen retained by livestock in growth  and N returned to subcatchments via excretion (excretaN) and estimated by:cut%N is the measured N content of cut herbage (g N kg−1 DM). Nitrogen retained by livestock in growth was calculated by:pA is the protein content of cattle or sheep according to intakeN that was not allocated to growth was defined as excretaN. N excretion is distributed as dung or urine according to c_dungN and c_urineN), and s_dungN and s_urineN):Nitrogen intake (cutN) was included in addition to livestockN as potential livestock output (PlivestockN) as follows:Subcatchments were not grazed and the herbage grown was cut and ensiled for feed over winter. This herbage (GNUE) and livestock production (LNUE) was derived for each sward treatment at the subcatchment scale as:To further assess the agronomic performance of the transitioning swards, the system NUE of both grass production (2.2NPPC), FYM applications and excreta from grazing animals (FYM+excretaC). Where NPPC was estimated as:The budget inputs were the net primary productivity (GPPC is gross primary production (gross uptake of CO2 via photosynthesis) and RaC are CO2 emissions from autotrophic respiration.where R soil), export of organic matter from herbage cuts and animal grazing (intake+cutC) and leaching of dissolved organic and inorganic C (leachingC). No data or simulation was available for methane (CH4) emissions from enteric fermentation nor animal excretion, therefore CH4 emissions from these sources were estimated according to IPCC Tier 2 and Tier 1 methodology respectively  at randomly chosen points within the subcatchment (based on a 25 x 25 m sampling grid). Fresh cut herbage was immediately weighed, and a representative sample taken and dried at 85 °C for a minimum of 24 h, to determine dry matter (DM) content. The DM content of the cut swaths was used to calculate yield on a per hectare basis. Nitrogen content of fresh herbage was determined across the grazing season using fresh snip samples. Snip samples were cut to a residual height of 5 cm following a W-shaped sampling strategy across cattle-grazed subcatchments, and subsequently stored at -18 °C prior to freeze-drying. Dried samples were ground and analysed for total N content using a Carlo Erba NA 2000 linked to a Sercon 20/22 isotope ratio mass spectrometer  and metabolisable energy content following determination of fibres .3) losses were not included in the NWFP observations, nor in the simulations. Consequently, we applied NH3 emission factors to the chemical fertiliser N, FYM and animal urine inputs (c_urineN and s_urineN), using UK-specific emission factors as described by Measurements of ammonia . Hence, for all 15 fields for the 6-year period, the cut yield biomass was validated with the experimental values. The amount of grazed biomass and excreta was compared and in very good agreement with the approach described in Section depositionN, fixationN, leachingN, and denitrificationN.The input data required for SPACSYS includes soil properties, weather condition , field management (ploughing), plant management (reseeding and cutting dates), fertilizer and FYM application , and animal management . All these are freely available on the NWFP data portal . The major source of N input, excluding HSGC and HSGC-T subcatchments, was chemical-fertiliser N (depositionN accounted for inputs of 21.2 kg N ha−1 a−1 (± 0.1 SE) across all subcatchments and ranged 19.2–23.5 kg N ha−1 a−1. Biological N fixation provided another N input in the HSGC and HSGC-T subcatchments where clover was sown, and in five of the ten RS replicates, accounting for 31.0, 31.0 and 12.2 kg N ha−1 a−1 of N inputs in HSGC, HSGC -T and RS respectively. Together fertiliserN, FYMN, depositionN, and fixationN supplied an average of 329, 324, 304, 257, 221, and 157 kg N ha−1 a−1 to HSG, HSG-T, PP, RS, HSGC, and HSGC-T respectively. The other major N input was excretaN from grazing livestock. Nitrogen excreta was greatest in the HSGC subcatchments at 105 kg N ha−1 a−1, and accounted for 55% of all N inputs, whereas it was lowest in the RS subcatchments at 38.7 kg N ha−1 a−1, accounting for just 16% of N inputs.Input of total N across all subcatchments and treatments ranged from 140 to 310 kg N hailiser N , which a−1 a−1 (mean of sward treatments). The majority of N was removed from the subcatchments as cutN (intakeN was included as herbage production (cutN plus intakeN), overall herbage production was greatest in the transitioning swards HSGC-T and HSG-T at 280 and 264 kg N ha−1 a-1 and least from the RS sward at 88 kg N ha−1 a−1, which was statistically similar to HSGC and HSG (p <  0.01). After cutN, intakeN was the second major N output from the subcatchments. However, only a fraction of intakeN was retained as livestockN, at 9.36, 8.75, 5.33, 4.42, 3.80, and 3.39 kg N ha−1 a−1 for the HSG, HSGC, HSG-T, PP, HSGC-T, and RS subcatchments respectively. When cutN was included as additional capacity for sward grazing (as PlivestockN) the system-livestock production increased to 18.4, 18.0, 14.6, 12.2, 11.6, and 6.3 kg N ha−1 a−1 for HSG-T, HSGC-T, HSG, HSGC, PP, and RS respectively (p =  0.06).Total N outputs ranged from 262 to 407 kg N ha as Ncut  for all denitrificationN and leachingN dominated. Denitrification accounted for 14.9–29.0% of N outputs, and leachingN losses accounted for 11.1–34.9% of N outputs. Overall, the greatest N outputs were observed from the PP subcatchments, followed by HSG-T, whereas the HSGC subcatchments had the lowest total N outputs .All other N outputs shown  can be d outputs . When N nce data . Only th3.2GNUE was extremely variable (GNUE was significantly greater (p <  0.001) for HSGC-T than for all other treatments, with the RS sward obtaining significantly lower GNUE values than all other treatments excluding HSG. LNUE for the different swards through their introduction (RS) and transition to established sward is shown in LNUE was typically very low at <10% for the PP, HSG and HSG-T swards, with an extreme LNUE of 3.5% observed from the RS subcatchments. In contrast, the HSGC-T and HSGC swards typically obtained LNUE values > 10%, at 42.2 and 32.4%, significantly greater than for all other swards (p < 0.001).variable , ranging3.3−1 a−1 for the field treatments, with the maxima observed on HSG and the minima on PP subcatchments (−1 a−1 respectively (p < 0.001). For most subcatchments, FYM plus excreta contributed 14% toward C inputs, however for the HSG-T and HSGC-T subcatchments FYM plus excreta was 8.5% of C inputs and 16% for the RS years.The C inputs considered under NPP ranged from 1.94 to 15.9 t C hatchments . Average−1 a−1, with RS significantly lower than all other treatments (p = 0.007), excluding HSGC which was statistically similar. Leached C and enteric and excretal CH4-C losses from all treatments ranged 75 to 353 and 18–60 kg C ha−1 a−1respectivey. Whereas soil respiration ranged 4.7 - 11.1 t C ha−1 a−1. For the C balance there were no significant differences between treatments. Generally, only the RS treatment acted as a C source at -1.53 t C ha−1 a−1, and the remaining treatments were all C sinks.Carbon outputs as cut herbage and intake from grazing livestock ranged 2.36–5.51 t C ha44.1−1 a−1 (n = 5), and the least for the HSGC-T treatment at 140 kg N ha−1 a−1. The range of values reported here are comparable to those reported for dairy systems by −1 a−1 respectively. Fertiliser N was the greatest N source for the PP, HSG, HSG-T and RS subcatchments at 128–177 kg N ha−1 a−1, which is greater than the average British values for cattle and sheep at 103 kg N ha-1  or via animal intake (intakeN), with values typically greater than the crop N output reported by −1 within the same region of England. However, when herbage outputs were converted to animal product (PlivestockN) the output (meat plus bones) became minor, ranging from 6.3 to 18.4 kg N ha−1 a−1 across all treatments. Although based on a different method for determining N content of LWG, the findings presented here are generally lower than those reported for a PP site in Scotland by LWG N outputs of 13–31 kg N ha−1 based on the daily LWG of heifers and lambs from 2004-2010. For the NWFP PP treatments, values ranged from 0 to 42 kg N ha−1 for individual subcatchments, with the lowest N outputs attributed to fields that were grazed by ewes alone and not primarily used for meat production.Unsurprisingly, the greatest N output from all NWFP treatments was as herbage, either cut (cutN plus intakeN) achieved from the HSG-T and HSGC-T subcatchments, which is also reflected in a more negative N balance for the HSG-T and HSGC-T years. However, unlike The transition of a grassland sward from PP to a new grass variety or mixed sward and the impact of this on the N balance is highlighted in denitrificationN . Studies which include gaseous N losses are scarce, particularly where gas speciation is included. To our knowledge the study by 2 and N2O losses were 14–45 kg N ha−1, whereas our denitrification values were greater at 61–76 kg N ha−1 a−1. However, our NH3 losses were lower at 4.1–9.0 kg N ha−1 a−1 relative to their combined NH3 and NOX losses at 36–68 kg N ha−1, although this may be due to the inclusion of NOX within the volatilisation estimate of x is included within the denitrificationN output in this study. Leached N losses (leachingN) were greatest during RS years, highlighting an additional N-loss pathway that should be accounted for when reseeding. Greater N leaching can be attributed to mineralisation of soil organic material following ploughing  at 36–92 kg N ha−1 a−1 were within the range of that reported by −1.After productive N outputs, the remaining N outputs can be described as N losses, the greatest of these was loughing , and the4.2GNUE values were extremely variable across management systems, but the mean values of 41–94% for the treatments receiving chemical-N fertiliser compared well to other GNUE values dependent on chemical-N fertiliser as a major N source, including 55–80% for cut swards (GNUE for the sward treatments dependent on fixationN as their N source (HSGC and HSGC-T) was much greater at 87–199%, which at the lower end was comparable to the organic dairy farm included in the study by The t swards ; 56–79% t swards ; 58% fort swards ; and 66%t swards . The NUELNUE was calculated and N outputs are only those gained within PlivestockN the LNUE falls below 50% and is often below 10%  suggest that NUE may not be the best metric for determining farm performance, especially where the end-goal is to reduce environmental N losses, instead they suggest that whole-farm N surplus targets would be a better metric for farmers to measure their performance by. The data presented within this study agrees with this recommendation, as NUE can vary significantly depending on N inputs. However, as shown in The 4.32, according to the SPACSYS simulations, except during the years where ploughing and sowing occurred where the RS subcatchments became a C source  and a net source of CO2 was during reseeding. Ploughing and cultivation disturbs soil aggregates, breaks soil structures and increases aeration and rate of decomposition. However, already in the first year of establishment of the new swards, the sink function was restored.The estimated values for NEE were -3.67, -3.37, and - 3.14 t C haasslands , establiasslands , extensiasslands  and inteasslands . The onl−1 for RS, HSG-T, PP, HSG, HSGC and HSGC-T respectively. Interestingly, the C sequestration increased from 0.45 to 1.13 t C ha-1 a-1 from the HSG-T years to the established HSG years. The opposite trend was observed for the HSGC treatment where C storage decreased from 2.57 to 1.96 t C ha-1 a-1 from the HSGC-T years to the established HSGC years. These estimated values are within the estimated ranges for European grazed and cut grasslands of 1.04 ± 0.73 t C ha−1  as well as the stimulation of primary production through grazing, C sequestration tends to be lower in cut compared to grazed systems . HoweverOur study confirms that C sequestration in grassland has the potential to partly mitigate the GHG balance of ruminant production systems at low grazing intensities . However5Our findings highlight the inefficiency of N use that can occur within grazed beef and sheep systems receiving chemical-N fertiliser, and the potential for increasing nitrogen use efficiencyin these systems. We were able to demonstrate the highly efficient use of applied N by the grass and grass with clover swards for all transitioning, established and permanent pasture sward treatments, ranging from 54 to 257%. However, this efficiency was markedly reduced when determined for livestock output, ranging from 7 to 42%. Nitrogen use efficiency was much reduced during reseed years, at 3 and 40% for the livestock- and grass-production systems respectively. Although yields increased in the first year following reseeding, we were not able to demonstrate any significant yield improvement for the reseeded treatments relative to the permanent pasture sward. However, grassland reseeding to include legumes within the new sward significantly enhanced nitrogen use efficiency of the livestock production system. Therefore, if the aim of grassland reseeding is to reduce N inputs and dependence on chemical-N fertiliser, then reseeding with legumes could provide a viable grassland management option.2 in most years, with C losses typically occurring during reseed years. Grazing can decrease CO2 uptake by plants due to a reduced leaf area index and animal respiration being a source of CO2, however the introduction of innovative sward varieties could reverse this effect and deliver higher C sequestration rates. Thus, improved grassland may help minimize the harmful environmental effects of C and N decoupling by ruminants, thereby providing environmental, animal welfare, nutritional and food integrity benefits.On an annual basis, our simulations suggest that the established swards acted as a sink for atmospheric CO


Uncontrolled replication is a process common to all cancers facilitated by the summation of changes accumulated as tumors progress. The aim of this study was to examine small groups of genes with known biology in replication and repair at the transcriptional and genomic levels, correlating alterations with survival in uveal melanoma tumor progression. Selected components of Pre-Replication, Pre-Initiation, and Replisome Complexes, DNA Damage Response and Mismatch Repair have been observed.Two groups have been generated for selected genes above and below the average alteration level and compared for expression and survival across The Cancer Genome Atlas uveal melanoma subtypes. Significant differences in expression between subtypes monosomic or disomic for chromosome 3 have been identified by Fisher’s exact test. Kaplan Meier survival distribution based on disease specific survival has been compared by Log-rank test.Genes with significant alteration include MCM2, MCM4, MCM5, CDC45, MCM10, CIZ1, PCNA, FEN1, LIG1, POLD1, POLE, HUS1, CHECK1, ATRIP, MLH3, and MSH6. Exon 4 skipping in CIZ1 previously identified as a cancer variant, and reportedly used as an early serum biomarker in lung cancer was found. Mismatch Repair protein MLH3 was found to have splicing variations with deletions to both Exon 5 and Exon 7 simultaneously. PCNA, FEN1, and LIG1 had increased relative expression levels not due to mutation or to copy number variation.The current study proposes changes in relative and differential expression to replication and repair genes that support the concept their products are causally involved in uveal melanoma. Specific avenues for early biomarker identification and therapeutic approach are suggested. Comparatively uncontrolled replication carried out by highly evolutionarily conserved multiprotein complexes, is a process shared by all cancers. Although many other processes including immortality, epithelial to mesenchyme transition, telomere metabolism, metastasis etc. contribute to tumorigenesis, the summation of genomic alterations in a tumor must facilitate replication. Duplication in the transformed cell is achieved at the expense of decreased fidelity, making replication a focal point where heterogeneity is created that upon clonal selection leads to tumor expansion and survival. Several individual critical replication genes have been examined in The Cancer Genome Atlas (TCGA) efforts and auxiliary studies, with computational methods placing alterations into known pathways to identify potential targets for precision medicine. However, the behavior of replication factors as a group did not receive analogous systematic investigation, likely because they are thought of as being part of a process rather than a pathway.TCGA has recently conducted an integrative analysis of uveal melanoma (UVM), and the data providing its molecular foundation are now available to the public as part of the “Rare Tumor Project” . While hAlterations indicative of replication stress (RS) correlating with aneuploidy, increased malignancy, and decreased survival have been observed, as well as changes to common replication components involved in bypass of replication stress. Generally RS is thought to be an early and strong driving force in tumorigenesis, and is seen over a broad spectrum of cancer types. It is defined as impediment to the replication fork that causes slowing or stalling of the replication machinery and is brought on by “exogenous” and “endogenous” factors . ExampleThis study specifically identifies changes in expression across UVM subtypes to MCM2, MCM4, MCM5, CDC45, MCM10, CIZ1, PCNA, FEN1, LIG1, POLD1, POLE, HUS1, CHECK1, ATRIP, MLH3, and MSH6. Resulting implications for bio-marker and therapeutics are discussed, and a rationale given for the observed alterations.The cluster analysis relied upon in this study was made by TCGA and is based on Somatic Copy Number Alteration (SCNA) . ClusterIn this report expression analysis is made by two algorithms, using the terms “differential” and “relative” to distinguish between them. Differential expression for UVM is defined as mRNA Z Scores (RNA-Seq by Expectation Maximization (RSEM)) (log2) compared to the expression distribution of each gene tumors that are diploid for the gene. The use of the diploid fraction is due to the lack of a normal control for RNA-Seq analysis . These data are calculated and made available by cBioPortal .This study defines “relative” expression as mRNA Z Scores (RNA-Seq Reads per Kilobase of Transcript per Million Mapped Reads (RPKM)) (log2) comparison to the UVM tumor cohort average in its entirety, also known as the “average alteration level”. These data have been determined specifically for this study and were made for both mRNA and individual exons.RNA-Seq derived exon expression levels were visualized in heat maps. The Gene Annotation File (GAF) “TCGA.hg19.June2011.gaf”  to creatn = 80, in each cluster n = 15 through 23) were examined, “q” values were not calculated, with the rationale that doing so might increase “type II” errors. Since placement of cases into “high” and “low” grouping was made relative to the total tumor cohort average, conjecturally, all cases “below” the tumor cohort average could also be “above” an average made from appropriate adjacent normal tissues, which was not available for RNA analysis in UVM samples. The procedure was evaluated using BAP1 and RPS19 as test genes.An output file containing Z scores for each exon was created and used to calculate an average Z score for each gene. This was regarded reasonable as structural variations to genes were found only with low frequency. UVM cases were sorted into two groups, those with average Z scores “above” and those “below” zero. After group designation, the cluster –4 each cThe TCGA UVM cohort was made up of eighty matched tumor and normal blood specimens. Tumors were obtained from patients that did not have previous systemic chemotherapy or focal radiation, with appropriate consent obtained from institutional review boards. A panel of five histopathologists with expertise in ocular pathology and melanoma, examined hematoxylin and eosin stained sections from paraffin embedded tumors defining tumor extent, cell morphology, pigmentation, mitotic index, and the presence of tumor-infiltrating lymphocytes and macrophages. The following information was also curated: “tumor status” (date of last contact), “vital status” , “date of last contact”, “date of death”, “cause of death”, “other cause of death”, “new tumor event after initial treatment”, “histology of new tumor event”, “site of new tumor”, “other site/new event”, “date of new event”, “additional surgery”, “additional treatment/radiation”, “additional treatment/pharmaceutical”.In principal four types of survival analysis can be made with TCGA clinical data. “overall survival” defined as the period from date of diagnosis until death from any cause, “progression-free interval” from date of diagnosis until the occurrence of an event in which the patient with or without the tumor does not get worse, “disease-free interval” date of diagnosis until first recurrence, and “disease specific survival” diagnosis date until death from the specific cancer type. All UVM survival curves constructed for this study were “disease specific survival” curves, as recommended by TCGA Pan Cancer Guidelines . Kaplan A total of thirty-seven genes Table  were obsP = 0.0001) (Table P = 0.0001). Half of the helicase complex components (MCM2–7) are located on chromosomes found by TCGA to have copy number alterations that include monosomy chromosome 3, 8q and 6p gains. A comparison of other genes in this study found on chromosome 3 indicates expression levels do not always simply correlate with SCNA  helicase components of the pre-replication complex is increased over a wide range of tumor types. The results for MCM2 3q21.3) specifically are given in Fig. q21.3 speP = 0.0018) .Conversely, MCM4 8q11.21) expression is increased in clusters 3 and 4, with escalation correlating to worse survival that correlate with SCNA gains to chromosome 8q in cluster 4 (1 expressP = 0.0019)  Fig. , Table 319) Fig.  and in R19) Fig. . CIZ1 in19) Fig. . Clear iPCNA, FEN1, LIG1, and HUS1 were found to have increased relative expression across clusters 1–4 Fig. . PCNA anP = 0.06, Log rank). POLE expression was not significantly different across clusters 1 through 4. Examining POLD1 and POLE clusters 3 & 4 to each other however, showed significant difference in the number of cases “above” and “below” the mean (P = 0.0042), indicating clusters 3 and 4 had more cases with higher than average POLE expression than POLD1  GNAQ and GNA11. It is a plausible hypothesis that alteration to these genes would directly or indirectly result in alterations to nucleotide pool availability in the nucleus, providing an early and endogenous cause of RS. Thought to be an early and driving event in cancer generally, there has been suggestion RS might be therapeutically targetable , 24.This study specifically found changes to the MCM helicase complex. MCM2–7 components effectually act to “license” the firing of origins of replication during G1-phase of the cell cycle, determining the multiple nascent origins that replicate during S phase. A recent comprehensive review has been made by Riera et al. . The comOnce the pre-replication complex is augmented with proteins CDC45 and GINS1–4 it is designated as a “pre-initiation complex”, CIZ1  is thought to function in this context . This inThe findings for CIZ1 led to identification a mononucleotide repeat in Intron 3 with possible MSI. Almost no evidence points to the involvement of MMR components as drivers for UVM, and examination of mutation rate does not show logarithmic elevation typical of MMR deficient tumors. Exon skipping was observed in MLH3 (MLH3 ∆ exon5/∆ exon7), a protein that interacts with MLH1 directly. MLH3 ∆ exon7 has been previously described  and in tReviewing relative expression for PCNA (Proliferating nuclear cell antigen), FEN1 (Flap endonuclease 1), and LIG1 (Ligase 1) an increase was found that implied their involvement in overcoming RS. These components of the replisome lacked mutation and SCNA but correlated none-the less with increased expression suggesting transcriptional mechanisms were used to overcome RS and fork collapse in UVM progression. PCNA is a ring shaped homo-trimer that encircles DNA . It inteTwo polymerases, POLD1 and POLE, important in the replication of B form DNA were selected for observation. The relative decrease in POLD1 expression in subtypes 3 and 4 and increase in POLE support a recently proposed model  in whichReplication stress activates DDR which prevents DNA damage from becoming fixed during replication and passed on in mitosis . It doesIn summary, this study examines selected replication and repair factors in UVM with known biology for expression differences with and without mutation across TCGA SCNA subtypes. Differences in expression have been observed that support the concept that these genes and their products are causally involved in UVM. These data suggest further avenues of research for biomarker identification and therapeutic approach.