Therefore wild cards were introduced into the Small Molecule Compound Library pattern, mixing it with the chitin-binding motif from Prosite (Prosite ID: PS00026), generating a more generalized arrangement, and the search through regular expression was done again. The sequences found by regular expression search were further submitted to Phobius [28] and SignalP 4.0 [44] for identification of signal peptides. Subsequently the signals were

removed and the mature sequences were submitted to InterProScan [47] for domain identification, the largest domain signature was chosen as the actual domain. The antimicrobial activity was predicted by a support vector machine (SVM) specific to cysteine stabilized peptides [46] and also by Collection of Antimicrobial Peptides (CAMP) algorithms [57]. In addition, RG7204 mw a multiple alignment was constructed by ClustalW [58], for verifying the similarities among the sequences. The LOMETS server [63] was used to find the best template for comparative modeling. In addition to the template indicated by LOMETS, the hevein-32 structure (HEV32, PDB ID: 1T0W) [1] was also used as a template, since it was solved in complex to N,N,N-triacetylglucosamine ((GlcNAc)3). The inclusion of this additional structure allows to identify the binding position of (GlcNAc)3 without docking experiments.

Therefore, two thousand theoretical three-dimensional models were constructed through Modeller 9.10 [14]. The (GlcNAc)3′s atoms were imported by setting as true the property io.hetatm from the class environ from Modeller 9.10. The final model was selected according to the discrete optimized

protein energy (DOPE) scores. This score assesses the energy of the model and indicates the best probable structures. If necessary, an additional energy minimization with two thousand cycles of steepest descent using the GROMOS96 implementation of Swiss-PdbViewer [17] was performed. The model with the best DOPE score was evaluated through PROSA II [61] and PROCHECK [35]. PROCHECK checks the stereochemical quality of a protein structure, through the Ramachandran plot, where good quality models are expected to have more than 90% of amino acid residues in most favored GNE-0877 and additional allowed regions. PROCHECK also gives the G-factor, a measurement of how unusual the model is, where values below −0.5 are unusual, while PROSA II indicates the fold quality. The electrostatic surface was calculated through APBS [5]. Surface potentials were set to ±5 kT e−1 (133.56 mV). Structure and surface visualization were done in PyMOL (The PyMOL Molecular Graphics System, Version 1.4.1, Schrödinger, LLC). Additionally, structural alignments were performed for verifying the structure similarities among the identified sequences through Dali Lite [18] and for verifying the similarities to structures deposited on PDB through Dali Server [23]. The assessment of structural alignments was done through Z-Score.

An ecologic study of CVD mortality from 1950 to 2000 in Chile highlights the importance of average versus peak exposures over time (Yuan et al., 2007). In check details this study, the most affected areas

had average arsenic levels of 90 μg/L prior to 1958, 879 μg/L from 1958 to 1970, 110 μg/L from 1971 to 1985, 40 μg/L from 1986 to 2000, and eventually <10 μg/L. Mortality risks were elevated for all circulatory diseases, hypertensive disease, and ischemic heart disease, but not for cerebrovascular disease. Rate ratios for acute myocardial infarction mortality in 1989–2000 for men born during 1958–1970 (3.23, 95% CI: 2.79–3.75) were higher than for men born in 1950–1957 (2.56, 95% CI: 1.26–5.18). Thus, average or cumulative exposure prior to assessment would not adequately reflect risk when part of the period involves very high exposure, along with possible life stage sensitivity. Studies involving populations with more constant, long-term exposure (e.g., Chen et al., 2011) are therefore preferable for evaluating health-protective doses for CVD. GSI-IX purchase Although the average exposure duration was estimated to be 25% of lifetime in Chen et al. (2011), the latency for heart

disease is considerably shorter than for cancer (Chen et al., 2011 and Yuan et al., 2007). Studies of populations with lifetime exposure from Taiwan (although limited by broad exposure ranges, Table 1) provide generally supportive evidence of the POD from Chen et al. (2011). A recent systematic review on arsenic exposure and CVD (Moon et al., 2012) examined the results from 31 population-based studies (22 high arsenic exposure studies predominantly from Taiwan and Bangladesh, and 9 cross-sectional or ecologic studies in low to moderate arsenic exposure areas including the United States). Methodological and clinical heterogeneity among studies were reported by the authors (variability in sample many sizes and in the referent groups (external versus internal) for comparison, differential CVD risk profiles between populations and exposure groups,

the use of aggregated exposure data or ascertainment at the individual level, and differences in the criteria used for the various cardiovascular outcomes). Meta-analysis of the low to moderate arsenic exposure studies resulted in pooled RRs that were statistically nonsignificant and significantly heterogeneous (CVD RR = 1.06; CHD RR = 1.06; stroke RR = 1.07; peripheral arterial disease (PAD) RR = 1.13; all p-heterogeneity <0.001). In contrast, the pooled RRs among the high arsenic exposure studies were statistically significant for CVD (1.32, 95% CI: 1.05–1.67), CHD (1.89, 95% CI: 1.33–2.69), and PAD (2.17, 95% CI: 1.47–3.20), but not for stroke (1.08, 95% CI: 0.98–1.19), in the overall assessment with noted limitations and statistical evidence of heterogeneity among studies ( Moon et al., 2012).

Therefore, a tagging

single nucleotide polymorphism (tSNP) set comprising variants −9731 G > T, −5848 T > C, +4860A > C, +8855 T > A, and +11015 T > G (rs1946519, rs2043055, rs549908, rs360729, rs3882891, respectively) was selected, based on haplotypes derived from the Innate Immunity PGA (IIPGA) Caucasian re-sequencing data (http://innateimmunity.net). The set was estimated to capture more than 90% of variation within the 21-kilobase IL18 region, stretching from 1 kilobase upstream to 300 base pairs downstream of JAK inhibitor review the gene. The set comprises three intronic variants (rs2043055, rs360729, rs3882891), a proximal promoter variant (rs1946519), and one synonymous single nucleotide polymorphism (SNP) (rs549908) within exon 4 which have been previously studied [15]. All five tSNPs were genotyped using TaqMan technology and probes designed by Applied Biosciences (ABI, Warrington UK). Fluorescence was measured with the ABI Prism 7900HT detection system analysed with the ABI TaqMan 7900HT v3.1software. Primers and MGB probes are available upon request. β-cell function and insulin resistance (IR) estimates were

derived using HOMA with the following formula: HOMA-IR = fasting insulin (μIU/ml) × fasting glucose (mmol/l)/22.5 [20], HOMA-β-cell = fasting insulin (μIU/ml) × 20/fasting Daporinad glucose (mmol/l) − 3.5 [21], quantitative insulin sensitivity check index (QUICKI) = 1/(log(fasting Bacterial neuraminidase insulin (μIU/ml)) + log(fasting glucose

(mg/dl)) [22]. The majority of statistical analyses were performed using Intercooled Stata 10.2 for Windows (StataCorp LP, USA). A χ2 test compared observed numbers of each genotype with those expected for a population in Hardy-Weinberg equilibrium (HWE). Data were transformed, when necessary to approximate a normal distribution. tSNPs were first analysed individually for association with baseline and post-prandial measures. Linear regressions were used for association analyses. Covariates were established using a backwards stepwise regression. Covariates for GENDAI included; height, age, gender, BMI and mean Tanner score. Covariates for EARSII included; BMI, smoking, age, region, and fasting levels when analysing post-prandial data. Covariates for GrOW included; age, estrogen use, smoking status, menopausal status and body fat %. P values less than 0.01 were considered significant. For the univariate analyses, setting a threshold of significance was the chosen method above Bonferroni corrections. Linkage disequilibrium (LD) between sites was estimated in Stata with the pairwise Lewontin’s D’ and r2 using the pwld function (http://www-gene.cimr.cam.ac.uk/clayton/software). Haplotype association analysis was carried out using THESIAS [23] and PHASE version [24].

Four products were equally Selleckchem Cisplatin detected as not irritating in CCM, AR and HSM (MPT products 1, 2, 7, and 10). Five products (MPT products 6–10) contain varying concentrations of dihydrogen hexafluorozirconate(2−) and hydrogen fluoride, which are presumed to be the major constituents responsible for corrosive/irritating effects. A systematic comparison of these products shows that overall the difference in concentration is reflected quite well in the results of the in vitro methods ( Table 5). The complete results for the nine individual compounds are shown in Table 2. The selection comprises

inorganic acids (sulphuric acid, 5%; phosphoric acid, 10% and Y-27632 manufacturer 25%), an inorganic acid salt (sodium silicate × 5H2O, 5%), an organic acid (citric acid × H2O, 20%), a salt of an organic acid (nitrilotriacetic acid (NTA) sodium salt, 10%), an alkanolamine (methanolamine (MEA), 5%), a solvent (diethylene glycol monobutyl ether (DEGBE), 20%) and a detergent (alkyl ether sulphate, C12–C14 with EO, sodium salt, 7%). Results from in vivo studies are listed as well in Table 2. In contrast to the testing strategy for products, the testing of individual compounds started for the majority of the compounds with the EpiDerm™ skin irritation test (all except for the detergent and 25% phosphoric

acid), based on the anticipated properties of the compound at the chosen concentration according to DSD. Regarding the latter aspect an exemption was made for the detergent since

it was of specific interest to investigate how this class of compound behaves in the in vitro corrosivity test Megestrol Acetate although a corrosive effect was not expected from DSD or in vivo data. Combinations of results from the different non-animal methods, grouped according to the outcomes for skin hazard classes (Table 6), show that from the seven samples with an extreme pH the classification based on in vitro methods matched directly with DSD classification in three cases (the inorganic compounds phosphoric acid, 10% and 25% and sodium silicate × 5H2O, 5%); in two cases the results of the in vitro methods indicated a more severe classification (the organic compounds citric acid × H2O, 20% and NTA sodium salt, 20%), in another two cases a less severe classification (an inorganic acid, (sulphuric acid, 5%) and the alkanolamine (MEA, 5%)). For the two samples with no extreme pH (the solvent DEGBE, 20% and the detergent alkyl sulphate C12–C14 with EO, sodium salt, 7%) the in vitro test confirmed the DSD-based classification as not irritating. Two of the HET-CAM results directly matched with DSD predictions (an inorganic and an organic acid (sulphuric acid, 5%; citric acid × H2O, 20%), cf. Table 2).

The striatum is infected later than PFC and hippocampus (Solbrig et al., 1994 and Solbrig et al., 1998), has less neovascularization and tissue remodeling (Solbrig et al., 2010), and similar viral quantification across groups in this study. We see “same virus, more pathology”, for example, increased ED1 staining per microscopic field in

striatum of WIN BI 6727 research buy and BD rats compared to HU-treated rats. Thus, in striatum, a structure normalized for virus, degree of inflammatory neuropathology is a reflection of anti-inflammatory efficacy of a drug treatment, not virus. In PFC, where neuropathology appears more advanced and vRNA numbers more divergent, there was no clear association between virus and either pro- or anti-inflammatory effects across the 3 groups. And finally, in hippocampus, HU produced a modest reduction in vRNA, with the mechanism of effect on virus not known. The multiple factors involved SAHA HDAC chemical structure in Borna Disease expression and progression under cannabinoid treatment cannot be completely reconciled using a single in vivo system, and a systematic approach integrated across several experimental domains will be required. Our current results introduce

the possibility that CB2 R agonist-induced changes at cellular, tissue, or systems level could have a role in reducing productive infections by BDV, may be generalizable to other neurotropic viruses, and provide a mechanism of neuroprotection beyond reduction of inflammation. Our results also improve upon past trials managing BDV encephalitis in rats with aggressive immunosuppressive therapy that resulted in dissemination and unusual distribution of virus beyond the CNS (Stitz et al., 1991). In summary, upregulation of CB2 expression under different pathophysiological conditions has been reported in several experimental paradigms and disease states with inflammatory or degenerative processes, diseases that have in common

glial activation, inflammation, oxidative/nitrative stress, and degeneration. Targeting of CB2 receptors with selective agonists is a new therapeutic avenue in inflammatory degenerative disorders for reduction of neuroinflammation. Our experiments show HU-308 activation of CB2 receptors, receptors known to be renewed SB-3CT during microglia proliferation and action, is a nontolerizing mechanism of controlling CNS inflammation during viral encephalitis and uses a nonpsychotropic cannabinoid agonist. Contrast with WIN will help inform decisions in use of newly developed cannabinoid agonists as accessory therapy. Male Lewis rats (Charles River Labs, Wilmington, MA, USA) were group housed on a 12 h light–dark cycle with ad libitum access to food and water. All experimental procedures were performed in compliance with the institutional (University of Manitoba) and Policy for the Humane Care and Use of Laboratory Animals guidelines.

Ultimately, by understanding fundamental aspects of RNA modification biology we will be able to develop selective and specific small-molecule Copanlisib cell line inhibitors to modulate RNA methylation levels. Such discoveries may well lead to the identification of novel

therapeutic strategies to treat complex diseases including developmental and neurological disorders, obesity or cancer. Papers of particular interest, published within the period of review, have been highlighted as: • of special interest We gratefully acknowledge the support of the Cambridge Stem Cell Initiative and Stephen Evans-Freke. We thank our funders Cancer Research UK (CR-UK)

(C10701/A15181), the Medical Research Council (MRC) (G0801904), the European Research Council (ERC) (310360), and EMBO (Grant no. ALTF 424-2008). “
“Current Opinion in Cell Biology 2014, 31:16–22 This review comes from a themed issue on Cell cycle, differentiation and disease Edited by Stefano Piccolo and Eduard Batlle For a complete overview see the Issue and the Editorial Available online 12th July 2014 http://dx.doi.org/10.1016/j.ceb.2014.06.011 0955-0674/© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). selleck compound Darwin’s theory of natural selection has revolutionized our understanding of how organisms evolve. Often, the essence of his theory is formulated with ‘the fittest survive’, a term first coined by Herbert Spencer, to summarize the ideas of Darwin Thalidomide that better adapted organisms will live to have more offspring. In 1881, zoologist

Wilhelm Roux argued that Darwinian competition and selection had not been considered for the development of tissues and organs. In his view, cells within our bodies were also likely to compete for space and limited resources. Such ‘fights’ among slightly varying ‘parts of our bodies’ would result in the ‘selective breeding’ of the most durable and the elimination of less durable parts (cells). Along similar lines, Santiago Ramon y Cajal proposed a few years later that developing neurons may be engaged in a competitive struggle for space and nutrition, an idea which gained support in the framework of the neurotrophic theory and the discovery of nerve growth factor by Rita-Levi Montalcini and its isolation by Stanley Cohen in 1960 [1]. During nervous system development, large proportions of neurons die in almost every region of the nervous system. The normal death of these neurons occurs during a limited time window coinciding with target innervation [2].

10,000.00 cells were counted per samples. Relative fluorescence intensities were monitored by BD FAXSCANTO™ flow cytometer (BD Bioscience, CA, USA) and analyzed by the software Modfit and Cell-Quest (BD Biosciences, Franklin Lakes, NJ) with settings of FL1 (green)

at 530 nm and FL2 (red) at 585 nm (Liu et al., 2007). Cellular ATP was determined by means of the firefly luciferin–luciferase assay system. Cells (1 × 105) were incubated as for the viability assay and suspension was centrifuged at 50 × g for 5 min at 4 °C. The pellet was treated with 1 ml of ice-cold 1 M HClO4. After centrifugation at 2000 × g for 10 min at 4 °C, aliquots (100 μl) of the supernatants were neutralized with

70 μl buy Bleomycin of 2 M KOH, suspended in 100 mM Tris-(hydroxymethyl) aminomethane (Tris)–HCl, pH 7.8 (1 ml final volume), and centrifuged again. Bioluminescence was measured in the supernatant with a Sigma-Aldrich assay kit according Doramapimod manufacturer to the manufacturer’s instructions, using an AutoLumat LB953 Luminescence photometer (Perkin-Elmer Life Sciences, Wilbad, Germany). Intracellular oxidation of dichlorodihydrofluorescein diacetate (H2DCFDA) to 2,7-dichlorofluorescein (DCF) by ROS was monitored through fluorescence increase. HepG2 cells were seeded in a 12-well plate at a density of 1 × 105 cells/well and incubated as for the cell viability assay. After incubations, the well plates were washed with PBS and then 100 μl/well pentoxifylline of 10 μmol/l H2DCFDA was added to each well, remaining incubated at 37 °C for 45 min in a 5% CO2 incubator. Fluorescence was measured in a model F-4500 Hitachi fluorescence spectrophotometer (Tokyo, Japan) at the 503/529 nm excitation/emission wavelength pair (slits 5/10 nm) (Halliwell and Whiteman, 2004). Mitochondria were isolated by standard differential centrifugation (Pedersen et al., 1978). Male Wistar rats weighing approximately 200 g were euthanized by decapitation; livers (10–15 g) were immediately removed, sliced in medium (50 ml)

consisting of 250 mM sucrose, 1 mM ethyleneglycol-bis(β-aminoethyl)-N,N,N′,N′-tetraacetic acid (EGTA) and 10 mM HEPES-KOH, pH 7.2, and homogenized three times for 15 s at 1 min intervals using a Potter-Elvehjem homogenizer. Homogenates were centrifuged (580 × g, 5 min) and the resulting supernatant further centrifuged (10300 × g, 10 min). Pellets were then suspended in medium (10 ml) consisting of 250 mM sucrose, 0.3 mM EGTA and 10 mM HEPES-KOH, pH 7.2, and centrifuged (3400 × g, 15 min). The final mitochondrial pellet was suspended in medium (1 ml) consisting of 250 mM sucrose and 10 mM HEPES-KOH, pH 7.2, and used within 3 h. Mitochondrial protein contents were determined by the Biuret reaction. Mitochondria were energized with 5 mM potassium succinate (plus 2.

2003). Immediately after the 96 h of SD, the rats (n=5 for each group) were

euthanized by decapitation, and the hippocampi were dissected and immediately frozen in liquid nitrogen. Tissues and serum were stored at −80 °C until use. Thereafter, the hippocampi were homogenized in lysis buffer (1% Triton X-100; 0.5% sodium deoxycholate; 100 mM Tris–HCl, pH 8.3; 150 mM NaCl; 10 mM EDTA; 0.1% SDS; 10% glycerol; 1% NP-40; and protease inhibitor cocktails), and the total protein concentration was determined using a protein assay kit (Bio-Rad, Hercules, CA, USA) ( Bradford, 1976). The samples were loaded learn more on 10% (PSD-95, 20 µg/lane; synapsin 1, synaptophysin and GAP-43, 30 µg/lane) SDS-polyacrylamide gels, separated using electrophoresis and then transferred to nitrocellulose membranes (Amersham GE, Little Chalfont, UK). Immunodetection was performed at room temperature. The membranes were blocked with 2% non-fat milk for 1 h and then incubated with primary antibodies for 1 h at the indicated dilutions: anti-PSD-95 (1:20.000); GAP-43 (1:5.000); synapsin 1 (1:1000); synaptophysin (Abcam, Cambridge, MA, USA; 1:10.000); anti-β-actin (1:10.000); β-tubulin (Sigma, St. Louis, MO, USA; 1:50.000). After 3 5 min washes, the membranes were incubated for 45 min with Alexa-680-conjugated anti-rabbit IgG (1:10.000, Invitrogen, Carlsbad, Seliciclib mouse CA, USA). After 5 5-min washes,

digital images of the membranes were acquired and quantified using the Odyssey Infrared Image System (LICOR, Baltimore, MD, USA). The band intensity of the protein of interest was normalized to the band intensity

of β-actin or β-tubulin. The relative protein expression in the SSD, Ex and ExSD groups was expressed as the percentage of the SC mean. Data were analyzed using SPSS (version 17.0), and in all analyses, p<0.05 was considered statistically significant. After confirmation of the normality of variables using the Shapiro–Wilk test, the values were compared using one-way analysis of variance (one-way ANOVA) followed by the Tukey post hoc test for both the western blotting and the behavioral PtdIns(3,4)P2 task data. Data were presented as the mean±standard error. Supported by CAPES, CNPq, CEPE, CEMSA, FAPESP, CEPID/SONO/FAPESP and INNT (Brazil). “
“Essential tremor is one of the most common adult movement disorders (Brin and Koller, 1998 and Louis et al., 1998), and can be characterized as tremor which is related to movements or postures of the limbs (Deuschl et al., 1998, Elble, 2006 and Elble and Koller, 1990). Recent studies have demonstrated substantial phenotypic variability in essential tremor, which may be a postural tremor or may include a substantial component of intention tremor (Deuschl et al., 2000 and Elble and Deuschl, 2011). This intentional component is poorly understood and has not been consistently associated with the measures of pathology, imaging, or central nervous system electrophysiology (Elble and Deuschl, 2011).

Resorption parallel to the bone surface fits the concept of a dynamic sealing zone allowing the OC

to resorb and move simultaneously [30]. It also fits Parfitt’s conclusion from histological observations that OCs travel across the cancellous bone surface and not just perpendicular to the bone surface as sometimes inferred [9]. Interestingly, the distinct resorption pattern consisting of trenches and pits corresponds to the two types of resorption events identified in vivo in human trabecular bone through SEM, i.e. so-called “longitudinally extended resorption” reflecting long lasting resorption and “reticulate patch resorption” reflecting short episodes of intermittent resorption interrupted by migration [10]. The model presented in Fig. 7 provides a mechanistic basis to explain how agents acting on the Ibrutinib manufacturer AZD6244 in vitro collagenolysis–demineralization balance may contribute to steering

the resorptive activity of the OC on the bone surface. The intrinsic efficiency of OCs to degrade collagen is smaller than their efficiency to demineralize it, as clearly shown through the collagen left-overs of control OC cultures on bone slices. Thus, hormones stimulating CatK and collagenolysis like glucocorticoids will make resorption more continuous [17] and [31] and conversely hormones inhibiting CatK and collagenolysis like estrogen [32], [33] and [34] render resorptive events shorter [16]. As speculated by others [35], it would be intriguing to investigate in a systematic way to what extent CatK gene expression can be regulated

independently of demineralization and OC activation. In this respect, it is of interest that the response of CatK expression to calcineurin inhibition is much weaker compared to that of agents involved in the demineralization process, like ClC7 and carbonic anhydrase II [36], and compared to TRACP, and β3-integrin [37]. Furthermore, the response of CatK to estrogens was shown to be higher than that of DC-STAMP, NFATc1 and c-fos [33]. Of note, D-malate dehydrogenase collagenolysis can also be regulated independently of demineralization by agents acting directly on CatK enzymatic activity. Examples are the local redox potential [38], local nitric oxide levels [39], and also the maturation stage of the collagen molecule. Here it is of interest that older collagen is degraded faster by CatK than young collagen [40], which fits the observation that older bone is degraded more extensively than young bone by cultured OCs [41]. One may speculate in the same way that mutant collagen from osteogenesis imperfecta is more efficiently degraded, which would then explain the high bone resorption level in this disease [42] and [43].

Error rates were computed from all trials. In a signal detection framework, we computed criterion and sensitivity (d′). Search slopes were computed for each individual and each combination of target emotion/target presence by linearly regressing all RTs on set size. We used ANOVA models in SPSS to analyse the control group, and to locate differences between patients and the control group. Because unequal variance in different

cells within the control population in an ANOVA design can increase type I error rates (Crawford and Garthwaite, 2007 and Crawford et al., 2009), we confirmed group differences and 2 × 2 interactions using a single-case Bayesian approach as implemented in see more Crawford’s software. Non-significant findings do not require confirmation. Note that for interactions involving a higher order or higher number of levels, no appropriate single-case Bayesian methods are available. In our control sample, set size, target emotion, and target presence influenced RT as shown previously (see Fig. 2A and Table 1), with a linear impact of set size. This result was confirmed by fitting a linear regression

Alpelisib order model to predict RT from set size, separately for each combination of target presence and target emotion. An ANOVA on search slope estimates (Table 2) underlines that search slope is influenced by target face – angry target faces have a shallower search slope – and by target presence. There were no effects in an ANOVA on intercepts of the regression model, as expected. Next, we compared the two patients with the control sample (Fig. 2A, Table 1). Patients

responded faster to happy than to angry targets, while healthy individuals showed the opposite pattern, in particular for larger set size (interaction Group × Set size × Emotion). This result was confirmed by comparing patients’ search slopes with the control sample which revealed a significant Group × Emotion interaction. On a single individual basis, Bayesian dissociation analysis revealed a significant Group × Emotion interaction for AM (p = .017) but not for BG. Further, patients showed slower RT and steeper search slopes overall. This was confirmed only as a trend in a single-case Bayes approach (one-tailed tests; RTs: AM, p < .05; BG, p < .10; search slopes: AM, p < .05; Rebamipide BG, p < .10). Patients also differed from the control group in a stronger non-linear effect of set size (quadratic interaction group × set size: F(1, 16) = 18.3; p < .005, η2 = .533) – RTs for the medium set size were disproportionately large. Reversal of the anger superiority effect in the patients’ RTs and search slopes might be due to a different strategy in a speed-accuracy trade-off. In this case, AM and possibly BG should show increased accuracy for angry as opposed to happy targets. Hence, we analysed errors using a signal detection analysis on sensitivity (d′) and response criterion for each combination of set size and target emotion (Table 2, Fig. 2B and C).