IM), intensely used mown pastures (IP) and extensively used pastures (EP), respectively. The obtained data were linked to above ground biodiversity pattern as well as water extractable fractions of nitrogen and carbon in soil. Shifts in land use intensity changed plant community composition from systems dominated by s-strategists in extensive managed grasslands to c-strategist dominated communities in intensive managed grasslands. Along the different types of land use intensity, the availability of inorganic nitrogen regulated the abundance of bacterial and archaeal ammonia oxidizers. In contrast, the amount of dissolved organic nitrogen determined the abundance of denitrifiers (nirS and nirK). The high abundance of nifH carrying bacteria at intensive managed sites gave evidence that the amounts of substrates as energy source outcompete the high availability of inorganic nitrogen in these sites. Overall, we revealed that abundance and function of microorganisms involved in key processes of inorganic N cycling  might be independently regulated by different abiotic and biotic factors in response to land use intensity.Understanding factors driving the ecology of N cycling microbial communities is of central importance for sustainable land use. In this study we report changes of abundance of denitrifiers, nitrifiers and nitrogen-fixing microorganisms  in response to different land use intensity levels and the consequences for potential turnover rates. We investigated selected grassland sites being comparable with respect to soil type and climatic conditions, which have been continuously treated for many years as intensely used meadows ( Soils provide a large number of ecosystem services including plant growth, carbon sequestration, degradation of xenobiotics and safeguarding of drinking water resources. Most of these functions are closely linked to the soil microbiome and its activity pattern Recently, the effects of land use changes have been studied mainly focusing on (i) conversion of grassland to forest or vice versa This intensification is also frequently observed in grassland ecosystems. While in the past sites have been used extensively as pastures, nowadays up to four times per season, the same areas are managed as meadows for hay production and silage, entailing an intensive application of organic and inorganic fertilizers. Differences in intensity of agricultural practice like mowing, grazing and fertilization lead to changes in plant composition The aim of the present study was to characterize microbial communities responsible for key processes in the inorganic nitrogen cycle  in grasslands of different land use intensity and relate these results to the aboveground biodiversity of plants as well as important below-ground properties (water extractable carbon and nitrogen fractions). Overall, we postulated that nitrogen cycling at extensively used sites is mainly driven by nitrogen fixation and internal nitrogen turnover is highly efficient resulting in low denitrification rates. In contrast, denitrification may play a more important role in nitrogen turnover at intensely used sites due to higher amounts of nitrogen available in soil and intensive plant growth .Experiments were carried out in the frame of the German Biodiversity Exploratories IM, three times manure application and two times mown per season), intensely used mown pasture  and extensively used pasture . All sites were sampled in early spring (April) as well as in summer (July) to assess the effect of season.Nine different grassland sites  were sampled categorized as follows: intensely used meadow  taken with a soil auger. All samples were cooled directly after sampling for DNA based analyses at −20°C and at 4°C for measurements of enzyme activities and soil parameter.www.biolflor.de). This concept describing the general limits to ecology and evolution based on the trade-off that organisms face when the resources they gain from the environment are allocated between either growth, maintenance or regeneration – known as the universal three-way trade-off.: (C) the survival of the individual using traits that maximise resource acquisition and resource control in consistently productive niches, (S) individual survival via maintenance of metabolic performance in variable and unproductive niches, or (R) rapid gene propagation via rapid completion of the lifecycle and regeneration in niches where events are frequently lethal to the individual. Based on this assumption the csr triangle has been defined, in which each plant could be integrated according to its individual life type strategies. Furthermore, site conditions were characterized by calculating the mean indicator values based on plant species composition according to At all investigated sites the vegetation has been recorded on an area of 4 × 4 m. Plants were identified on taxa level and their percentage cover was estimated separately for the shrub layer (0–5 m woody species) and the herbaceous layer (including phanerophyte seedlings), respectively (data here not shown). The ecological strategy type of each vascular plant according to 2 extracts of the soil samples were prepared to analyze carbon and nitrogen contents in water extractable fraction. Therefore soil was mixed with 0.01 M CaCl2 at a ratio of 1∶2 3–N) and N°3 (NH4+-N) .CaClPotential nitrification rates (PNR) were determined via micro titer plate assay as described by 3), were flushed with helium in airtight serum bottles. Subsequent acetylene was added to the slurries and the bottles were shaken via overhead shaker at 25°C for two hours. Every hour gas samples of headspace atmosphere were taken with a gas-tight syringe, and N2O concentrations measured using a gas chromatograph .Potential denitrification activity (DEA) was determined using the anaerobic slurry technique as described by Genomic DNA was extracted from 0.5 g bulk soil (wet weight) using FastDNA Spin Kit for soil  according to manufacturer’s protocol. Quality and Quantity of DNA extracts were determined with Nanodrop 1000 Spectrophotometer . A260/A280 ratios were approx 1.9. As the used kit is known to highly influence A230 due to the used binding matrix which has its absorption maximum at 230 nm, we used qPCR dilution series to exclude inhibition effects and did not calculate A230/260 ratios.Quantitative real-time PCR (qPCR) was conducted on a 7300 Real-Time PCR System  using SybrGreen as fluorescent dye to estimate the abundance of ammonia oxidizing bacteria and archaea, nitrogen fixing microbes as well as nitrite reducers. The marker genes used as well as the corresponding conditions for qPCR are listed in nirS and nirK dimethyl sulfoxide (DMSO) was additionally used . The PCR reaction was started with a hot start of 94°C for 10 min according to the manufacturer’s instruction of the SybrGreen master mix, followed by 40 cycles with specific temperature profile according to the gene targeted (see 6–101 gene copies/µl) containing the respective cloned gene (see (-1/slope) - 1] and resulted in values from 97% to 83%.In a pre-experiment a dilution of the DNA of 1∶64 turned out to be best suited avoid inhibition of PCR, e.g. by co-extracted humic substances (data not shown). Quantitative real-time PCR was performed in 96–well plates (Applied Biosystems). Reaction mixtures with total volume of 25 µl were composed of 12.5 µl of Power SybrGreen PCR Master Mix , 15 µg bovine serum albumine , gene specific concentrations of the forward and reverse primer (Metabion Germany) see  and 2 µleted see . Data cogene see . The amp-1). Using the R environment for statistical computing (http://www.R-project.org) univariate and multivariate methods were applied to test the effect of land use and season. Assumptions were tested by checking the residuals . To account for the existence of replicate groups (n = 5) in each case linear mixed-effect models (lme in package nlme) were fitted. Two factor lmes with pairwise comparisons were used to construct a table of p-values. The integrative multivariate data analysis was based on a between group analysis (between in package ade4).All results were related on the basis of one gram of soil dry weight (g dwTo investigate differences in plant diversity data a one factor ANOVA (lm in R) and subsequent pairwise t-tests with adjusted p-values (pairwise.t.test with method holm) were applied.IM and IP) compared to the EP plots where 49 different species were found (p = 0.019). The coverage of vascular plant was between 102 and 170%, with highest plant coverage on the IM plots. The plant species composition showed a clear dependency on the land use intensity level (EP and IP) were more colonized by herbs (84 to 107% of coverage) indicating that grasses are more adapted to the disturbance caused by mowing. Considering the different ecological strategy types of the collected plants we found a clear trend for plants more linked to r-strategists on the extensively used sites (up to 96% of coverage) to c-strategists on the intensely used sites (up to 99% of the coverage).The number of plant species on the investigated sites ranged between 17 and 58. Significantly lower numbers were observed on the intensively managed plots . In July values were consistently low (0.19 and 0.53 µg N g−1 dw) in dependent from the site, whereas in April (0.62 to 4.1 µg N g−1 dw) differences between sites were more pronounced, with a tendency for higher values in soil samples from EP and IM compared to IP (p = 0.2976). Nitrate concentrations were in the range of 1.61 and 54.5 µg N g−1 dw in April and 1.10 and 18.2 µg N g−1 dw in July. No significant influence of land use and time was detected , however samples taken in April showed a tendency for higher values than those taken in July. Water extractable nitrogen (TNb) contents were strongly linked to the nitrate concentration with values from 4.85 to 62.6 µg N g−1 dw in April and in 4.09 to 23.3 µg N g−1 dw in July. WEOC values ranging from 30.6–57.4 µg C g−1 dw were neither affected by land use intensity (p = 0.9706) nor by season (p = 0.6837). Data are summarized in Soil ammonium content ranged from 0.19 to 4.1 µg N gIM sites. However transformation rates between the three investigated sites (AEG1 - 3) were highly variable with values from 0.28 up to 2.55 µgNO2− N g−1 dw h−1 (IP ranged between 0.26 and 1.91 µgNO2− N g−1 dw h−1. and rates were neither significant different to IM nor to EP (p = 0.2944) plots. Again a high variation of the values, comparing the three sites under investigation (AEG4 - 6), was visible. The lowest PNR was measured in samples derived from EP (0.06−0.31 µgNO2− N g−1 dw h−1), whereas a significant difference between EP and IM was observed (p = 0.0327). Overall a significant decrease of PNR from intensely to extensively managed sites was found (p = 0.0350). The sampling time point did not influence PNR in the investigated samples (p = 0.4882).The highest PNR values were measured in samples derived from 1 dw h−1 . PNR in IM and EP were up to the factor of three times higher (ranging from 0.31 to 2.39 µg N2O− N g−1 dw h−1) compared to summer (0.25–1.46 µg N2O– N g−1 dw h−1) indicating a higher denitrification activity in soil at the beginning of the vegetation period. In samples from IP the contrary phenomenon was observed. Here higher values were measured in summer (0.99 to 1.34 µg N2O− N g−1 dw h−1) than in spring (0.55 to 1.22 µg N2O− N g−1 dw h−1). Overall a significant positive influence of land use intensity was proven (p = 0.0009).In contrast DEA showed an interdependency between the sampling time point and the land use intensity  increased significantly from the spring to the summer sampling at all intensively managed sites (IM and IP), ranging in April from 4.4×106 to 5.5×107 copies g-1and in July from 2.2×107 to 2.1 108 copies g-1  a decrease from April to July was observed whereas on the third one an increase was found. Overall, AOB community size was influenced by season (p = 0.0293) and by land use intensity (p = 0.0004). Gene copy numbers for the archaeal amoA gene (AOA) ranged from 3.0×106 and 2.5×108 g−1 in April and from 8.5×106 and 4.5×108 copies g−1 in July; no statistically significant seasonal effect were proven (p = 0.5872), but a significant increase from intensely to extensively used plots was observed (see amoA gene abundances on the intensely used plots and a decrease on the extensively used sites was visible determined. Considering the ratios of AOA and AOB except the site AEG6 (AOA:AOB ratio <1) AOA : AOB ratios between 2.1 and 16 were found indicating a dominance from AOA over AOB. However in soil samples from the intensely used sites the ratio decreased significantly from April to July, whereas in samples from EP in two plots no changes were observed between the two sampling time points and a increase from 2.2 to 16 was found in the 3rd plot (AEG7).Gene copy numbers of bacterial pies g-1 . Gene coed sites . In addirved see . As for nirK ranged in April from 8.9×107 to 3.7×108 copies g−1and increased in July ranging from 5.0×108 to 3.4×109 copies g−1  revealed up to ten times lower copy numbers than AEG1 and 2 and AEG8 (belonging to EP) up to five times higher numbers than the other both plots of this land use category. Overall nirK gene abundance increased significantly with increasing land use intensity. The occurrence of nirS genes was significant lower in spring than in summer (p = 0.0315). Copy numbers ranged from 2.3×106 and 1.4×107 copies g−1 in April and between 4.0×106 and 2.9×107 copies g dw-1 in July. Also land use had a significant influence on the number of nirS gene copies. However nirS gene copy numbers were up to the factor of 500 lower compared to nirK.Gene copy numbers for nifH ranged from 1.8×106 to 6.9×107 copies g dw−1 (IP sites show significant higher nifH copy numbers (from 1.9×107 to 6.9×107 copies g dw−1) than the other two land use categories. The lowest values were detected on the EP plots, where quantities between 1.4×106 and 9.0×106 copies g dw−1 were measured.Gene copy numbers for s g dw−1 . The datIM and IP) and the extensively (EP) managed grassland sites to a large extent (PCA1∶43% PCA2∶24%). Based on the BGA data, it can be assumed that the effects of fertilization were more dominant compared to the influence of grazing and mowing. Moreover, clear differences between the sampling time points appeared . Overall abundance of nifH, amoA AOA and AOB genes as well as potential nitrification activity were more related to land use intensity, whereas ammonium content and dissolved organic carbon as well as DNA content and abundance of nirS and nirK genes were affected more by time.Based on all data obtained a between group analysis (BGA) was performed using the mean values of all five replicates from each site . The BGA−1 is linked to a 50% higher productivity and a decrease of species richness by 30%.amoA gene copy numbers in samples from the IP and IM from spring to summer no comparable increase in PNR activities was observed. This could be the result of the fact that during summer the soil pH mainly in the rhizosphere decreased due to higher exudation rates of the plants, which might reduces the ratio of ammonia to ammonium in soil. As ammonia oxidizing microbes are only able to use ammonia for oxidation amoA gene copies when data from the spring and summer sampling were compared. AOA harbor urease genes allowing AOA to use urea as alternative substrate, when ammonia is limited in soil Several studies which investigated the impacts of different management regimes on the nitrifying potential in soil amoA AOA than amoA AOB copies (AOA/AOB ratios were between 2 and 16) was found. These results are in accordance with some other recent studies indicating dominance from AOA over AOB in soils amoA genes and the nitrification potential (R(AOA) = 0.836, p(AOA) <0.001 and R(AOB) = 0.680, p(AOB )<0.001) our dataset delivers one more hint that AOA plays an important role at least for ammonia oxidation in soil, although based on molecular data still the oxidation of hydroxylamine to nitrite has not been proven for AOA. Overall it seems that AOA and AOB are similarly affected by land use intensification and it has been crystallized that nitrogen availability is the major driver for performance and abundance of the nitrifying community.In nearly all samples a higher number of EP, where plant biomass was significantly lower. However in our study DEA was not stimulated in summer compared to spring, which is surprising on the first glance but might be a result of a low nitrate concentrations found mainly in soils from intensively managed plots IP and IM. Furthermore the differences in water content and the corresponding differences in the redox potential between spring and summer sampling might have influenced DEA activity independent from land use intensities.Microbial communities in soil are generally stimulated by plant growth especially in the rhizosphere by root exudates, resulting in an increase of anoxic habitats in the rhizosphere and the need for microbes to use alternative electron acceptors like nitrate inducing denitrification nirK and nirS) were quantified. Overall, many prokaryotes from soil are able to denitrify and the proportion of denitrifiers within the soil microbial community was considered between 10% and 60% of the total bacterial and archaeal microbiota reaching values between 105 and 109 copies g−1nirK genes dominated over nirS genes (nirS/nirK ratio 0.002–0.02). This is in accordance with data from other studies, which indicate nirK phylotypes being more dominant in the rhizosphere or in soils characterized by an intensive root development strong time effect (p<0.0001) was found for both types of nitrite reductase genes in all soil samples, with values up to 20 times higher in summer, which correlated to the increased overall microbial biomass values obtained in soil samples from summer compared to spring (data not shown). The increase in abundance of the nitrite reductase over time is in contrast to the potential measurements of denitrification, but could be easily explained as all denitrifiers are facultative anaerobes and can shift their metabolism from respiration to denitrification based on the conditions present in soil. Therefore not surprisingly this missing link between denitrifying activity and genetic potential was described in some studies before To describe denitrifying communities, the two nitrite reductase genes  were nitrogen is limited. Despite no differences were found in the WEOC, this might be related to the fact that in soil the amount of energy equivalents needed for nitrogen fixation is too low in soil samples derived from EP nifH as well as low potential activity for nitrogen fixation was found at the youngest sites, which had the lowest supply with nitrogen and lacked of energy equivalents. Surprisingly the highest amount of nifH gene abundance, so we assume that the obtained results just reflect the occurrence of free living nitrogen fixing microbes, as we did not include nodule analysis in our study.However it must be stated that the occurrence of legumes was not correlated to the Obviously there is a strong link between land use intensity and microbial community structure in soil. Some of these effects might be direct effects and a response to e.g. increased fertilization regimes, other might be indirect effects and mediated by changes in the plant diversity and biomass (e.g. through changes in exudation). However the described data just reflect potentials for certain processes. If the same response pattern can be confirmed for the activation of certain functional traits remains an open question for future research focusing more on mRNA based studies with a more dense net of sampling time points.Table S1List of plant species with species categorization according to ecological strategy type (Grime 1997) and Ellenberg values for moisture (M) and nutrients (N) . Different ecological strategy types as given in the table are competitors (c), competitive ruderals (cr), stress-tolerant competitors (cs), stress-tolerant ruderals (sr), csr-plants (competition is restricted by the combined effects of stress and disturbance), stress-tolerants (s) and ruderals (r). Ellenberg indicator values are normalized values on an ordinal scale from low to high values (1 to 9) whereas 1 means high tand 9 low tolerance to nutrient- and moisture stress. NA shows plants which are not categoriesed by Ellenberg and x signs these with a large ecolocial amplitude. Occurence of each plant species on the different plots is given in percent of coverage.(DOC)Click here for additional data file.


This was then transferred to the surface of a bare gold screen-printed electrode (SPGE) and detection was performed by chronoamperometry, monitoring the reaction of 3,3’,5,5’-Tetramethylbenzidine dihydrochloride (TMB) and hydrogen peroxide (H2O2) catalysed by HRP at −100 mV potential vs. onboard Ag-AgCl pseudo-reference electrode. The immunosensor exhibited detection limit of 5 µg·L−1 fumonisins with a dynamic range from 1 µg·L−1–1000 µg·L−1. The sensor also performed well in extracted corn samples. An electrochemical affinity sensor for the determination of fumonisins mycotoxins (Fms) using monoclonal antibody modified screen-printed gold electrode with carbon counter and silver-silver chloride pseudo-reference electrode is reported in this work. A direct competitive enzyme-linked immunosorbent assay (ELISA) was initially developed, exhibiting a detection limit of 100 µg·L Fusarium genus have become one of the most widely researched areas of mycotoxins contamination in food. These toxins were first isolated from a culture of F. verticillioides by Gelderblom and co-workers  − 0.63 with a limit of detection (LOD) of ~5 µg·L−1 . In this case, the LOD obtained in this final optimized assay system with the pre-incubation step was lower than reported previously. This sensor proved to be very sensitive and for low level of fumonicins detection in buffer solutions. Using this device and technique with highly contaminated food samples (above 500 µg·L−1) may require the sample to be diluted to obtain the toxin concentration. 1 and FB2. The samples were first extracted using methanol/water mixture (70:30) and then either first used without any further treatment or the second were cleaned and concentrated using a clean-up procedure (C-18 SPE column). Samples were then dried and resuspended in PBS buffer and quantified using the developed electrochemical immunosensor assay. Other none spiked corn samples were also extracted and determined in order to evaluate possible matrix effect compared to fumonisins in buffer samples. For non spiked samples background signal, the current archived was found to be similar to the signal normally achieved in buffer (data not shown). This indicates that there is minimum matrix effect on the background electrochemical signal of the immunosensor by following the developed procedure. In spiked corn samples, the recovery of fumonisins was determined from the analyzed concentrations of fumonisins in the corn samples, compared to the expected values spiked to the sample. Using a calibration curve of fumonisin in a non spiked corn extract and C-18 cleaned samples, the obtained recovery results for spiked samples were calculated and these are shown in To test the performance of the sensor in real sample matrix and conduct a recovery test for fumonisin, ground corn samples were spiked with various concentrations of mixed FB−1) obtained less recovery compared to higher concentration (500 and 2500 µg·kg−1). This may suggest that substances in the samples containing low fumonisins inhibited the toxin signal response. By using a clean-up procedure (C-18 SPE column) which will help in removing these substances, the recovery of fumonisins have increase due to their removal from the sample extract. According to Muscarella et al.  + d F was defined as the concentration of toxin equivalent to three times the value of standard deviation (σ). This was calculated based on the following equation:x [a – d/ (a – d) − 3σ]b−1/LOD = Where σ is the standard deviation of the zero value.Samples preparation and extraction were conducted following the procedure as described by the Veratox ELISA kit . A corn sample was first ground and then mixed thoroughly before adding different concentrations of fumonisins standard (spiked). A 5 g of ground corn sample was used, mixed with 25 mL of 70% methanol and 30% water and then shaken vigorously for 3 minutes. The extract was filtered through a Whatman #1 filter paper to remove the solid material and the filtrate was then collected as the sample for analysis without further preparation. For this procedure, samples extraction was conducted similar to the procedure described above. Sample filtrates were collected and cleaned using C-18 SPE  following the procedure accompanying the SPE columns. The C-18 SPE column was first conditioned by sequentially passing 5 ml methanol and 5 mL water through the column. A 4 mL of sample filtrate was then passed through the column, followed by 6.0 mL deionised water. The fumonisins was then eluted from the SPE column by rinseing with 2.0 mL of methanol:water (70:30). The sample eluted was dried under nitrogen stream, re-dissolved in 0.01 M PBS pH 7.4 before 10 µL of samples used for the ELISA or immunosensor analysis.1 and FB2). A spectrophotometric immunoassay test was first developed in order to optimise the assay conditions and concentrations before moving the test on the surface of the electrochemical sensor. The final developed SPGE immunosensor exhibited a working ranges from 10–1000 µg·L−1 and the LOD was ~5 µg·L−1. This is well below the detection limit required for EU legislation of 2–4 mg·L−1 of Fms . The sensor was also examined in extracted and cleaned up (using C-18 SPE columns) corn samples and showed very low matrix interference, high sensitivity and reproducibility. We have demonstrated that a competitive immunoassay for Fms, on an electrochemical screen-printed sensor device is capable of detecting fumonisins in corn samples and therefore has a great potential for on-site and rapid analysis. An electrochemical immunosesnor based on gold working electrode using screen-printed sensor was developed for the detection and analysis of fumonisins in food samples such as corn. A monoclonal antibody against fumonisins was applied in this work in order to enable the analysis of fumonisins toxins (FB


Animal studies are very useful in detection of early disease indicators and in unravelling the pathophysiological processes underlying core psychiatric disorder phenotypes. Early indicators are critical for preventive and efficient treatment of progressive psychiatric disorders like anorexia nervosa. Comparable to physical hyperactivity observed in anorexia nervosa patients, in the activity-based anorexia rodent model, mice and rats express paradoxical high voluntary wheel running activity levels when food restricted. Eleven inbred mouse strains and outbred Wistar WU rats were exposed to the activity-based anorexia model in search of identifying susceptibility predictors. Body weight, food intake and wheel running activity levels of each individual mouse and rat were measured. Mouse strains and rats with high wheel running activity levels during food restriction exhibited accelerated body weight loss. Linear mixed models for repeated measures analysis showed that baseline wheel running activity levels preceding the scheduled food restriction phase strongly predicted activity-based anorexia susceptibility (mice: Beta  =  −0.0158 (±0.003 SE), P<0.0001; rats: Beta  =  −0.0242 (±0.004 SE), P<0.0001) compared to other baseline parameters. These results suggest that physical activity levels play an important role in activity-based anorexia susceptibility in different rodent species with genetically diverse background. These findings support previous retrospective studies on physical activity levels in anorexia nervosa patients and indicate that pre-morbid physical activity levels could reflect an early indicator for disease severity. Eating disorders are severe psychiatric illnesses with high morbidity and mortality. Particularly anorexia nervosa (AN) has the highest mortality rate among psychiatric disorders Physical activity levels are important in AN; they play a role in the onset and maintenance of illness ad libitum available for a limited period at the same time during several consecutive days Animal studies take place under controlled genetic and environmental conditions, minimizing their complex interaction effects on phenotypic heterogeneity. They may provide novel insights in pre-morbid factors that can affect and/or predict the course of the disorder. This is a challenge in human studies, as currently the research on these factors is determined either via cross-sectional or retrospective studies. Longitudinal prospective studies are very sparse (e.g. Nicholls & Viner Previous studies performed in our laboratory mapped the hyperactivity behaviour in chromosome substitution (CS) strain mice and has identified CS strains becoming hyperactive or being susceptible to this model All animal experiments were approved by the Animal Experimentation Committee of University Medical Centre Utrecht and were carried out in agreement with the Dutch Law  and European regulations (Guideline 86/609/EEC).http://phenome.jax.org/db/q?rtn=strains/search&reqpanel=MPD). Following weaning at 3–4 weeks, female and male mice were separately housed in groups (2–5 mice per cage) in cages . Outbred Wistar WU rats (n = 34) were obtained from Harlan  and were individually housed upon arrival. The housing facilities were maintained on a 12:12 h dark/light cycle with an ambient temperature of 21.0±2°C and humidity of 55±10%. During this period, the mice and rats were given water and food ad libitum . For this study we used test-naive 3–4 months old female mice and female rats, because of the high prevalence of AN in females Initial breeding pairs of the inbred mouse strains were obtained from The Jackson Laboratory . The mice used in the experiment were bred at the Rudolf Magnus Institute of Neuroscience animal facility. The following strains were tested: A/J (n = 8) (JAX stock # 000646), AKR/J (AKR n = 8) (JAX stock #000648), BALB/cByJ (BALB n = 7) (JAX stock # 001026), C3H/HeJ (C3H n = 12) (JAX stock # 000659), C57BL/6J (B6J n = 10) (JAX stock # 000664), CAST/EiJ (CAST n = 8) (JAX stock # 000928), DBA/2J (DBA n = 7) (JAX stock # 000671), FVB/NJ (FVB n = 8) (JAX stock # 001800), KK/HlJ (KK n = 10) (JAX stock # 002106), NZW/LacJ (NZW n = 10) (JAX stock # 001058) and WSB/EiJ (WSB n = 10) (JAX stock # 001145). The selected strains are part of Mouse Phenome Database (MPD) priority strains, Tier 1  in the abdominal cavity under fentanyl–fluanisone  and midazolam  anaesthesia. After surgery, rats were treated with carprofen  and saline  and allowed to recover for 2 weeks. With these transmitters, the locomotor activity, while not in the wheel, was measured continuously.ad libitum access to food, water and running wheel, they were exposed to a daily restricted feeding schedule (restriction phase); mice for 4 consecutive days and rats for 5 consecutive days. The adaptation phase was 7 days for mice and 10 days for rats; from our previous experiments it was observed that rats need few more days to adapt to the presence of a wheel. During this period, food  per gram) was available only in the beginning of the dark phase for 2 hrs for the mice and 1.5 hrs for the rats.After an adaptation phase in individual cages where the rodents had Baseline measures for body weight and food intake were generated on the basis of the average value during the last three days of the adaptation phase (measured just before the dark period). During these days, the animals were left undisturbed until the start of the restriction phase. Baseline wheel running activity (wheel revolutions) (WRA) levels were determined as the average WRA of the last two days of the adaptation phase, just before the restriction phase, when the activity levels are stable. WRA levels were measured daily during the restriction phase.The transmitters implanted in the rats were switched on by magnetic field induction during the baseline phase of the experiment. Baseline locomotor activity levels (LMA) were determined as the average LMA of the last two days of the adaptation phase. The recording was continuous during the restriction days. For a group of rats (n = 6) because of a technical malfunction, there were no LMA recordings for days 2 and 3 of the restriction phase.The level of body weight loss during the scheduled food restriction days was selected as a measure of susceptibility to the ABA. In mice, if a mouse lost 15% or more of their baseline BW on any experimental day, that mouse was taken out of the experiment  and was considered susceptible to ABA. In rats, if a rat lost 25% or more of their baseline body weight on any experimental day, that rat was taken out of the experiment  and was considered susceptible to ABA. Based on the level of body weight loss, a second dichotomous variable for ABA susceptibility was generated, with each individual mouse and rat assigned a value whether it was susceptible or not. In this way, a percentage of mice within each strain and a percentage of rats that were susceptible to activity based anorexia model (ABA) i.e. that could not maintain the body weight above 85% (mice) and 75% (rats) of their baseline body weight, was determined.The data were expressed as means with standard error of the mean (SEM) unless otherwise specified. One sample Kolmogorov-Smirnov test was used to check the Gaussian distribution and Levene's test was used for the homogeneity of variances.To assess the effects of the baseline parameters  as predictive factors, linear mixed models for repeated measures analysis, which takes into account the longitudinal data and within-mouse correlations, were used. To analyze the susceptibility to the model, body weight loss during the restriction phase was used as the dependent variable. Animal's identification number was entered as random effects, while in the fixed effects, the days of the experiment and each of the baseline factors were entered. To check whether there was a specific mouse inbred strain that contributed to the effect of these factors, the analysis was repeated with each of the mouse inbred strains being taken out of the analysis in turns.Two-sided P values of <0.05 were considered statistically significant. The effects were expressed as standardized beta (Beta) with standard error (SE), denoting the change in dependent variable with one standard deviation change of the independent variable. Spearman's coefficient of rank correlation was used for correlation analysis as the data were not normally distributed. Significance was set at P<0.05.Statistical analysis was carried out using SPSS 15.0 for Windows .There was a wide distribution of baseline body weight between strains. However, the majority of the strains had a baseline body weight (BW) between 20 g and 26 g. CAST and WSB mice had the lowest BW with 13.72 g (±0.09 SEM) and 16.27 g (±0.24 SEM), respectively. On the other side of the baseline body weight spectrum are NZW and KK strains with a BW of 32.99 g (±0.71 SEM) and 38.94g (±0.92 SEM), respectively . Food inThe rats' baseline body weight ranged between 194 and 253 g, and their food intake was between 55.38 to 81.33kcal. Food intake corrected for body weight was between 0.25 to 0.37kcal (data not shown). Baseline WRA levels were from approx. 270 to 13400 corresponding to 0.3 to 13.4 km. Non-specific locomotor activity levels (measured via the telemetry system) were between approx. 525 and 6500 counts (data not shown).Nearly half of the strains were particularly susceptible in this model. Within these strains, less than 25% of the mice remained at a BW level above 85% of their baseline BW until the end of the experimental days .Linear mixed models for repeated measures analysis revealed that among baseline factors , WRA levels had the strongest effect on ABA susceptibility (body weight loss), based on Beta estimate (Beta  =  −0.0158 (±0.003 SE), P<0.0001). BW was the second factor (Beta  = 0.0118 (±0.003 SE), P = 0.0005) and FI was third (Beta  =  −0.0102 (±0.004 SE), P = 0.0057). The derived baseline FI/BW had also a significant effect (Beta  =  −0.0106 (±0.004 SE), P = 0.0041). The interaction of the derived FI/BW and baseline WRA levels (FBxW) had also a strong effect on ABA susceptibility, where the effect of WRA levels was increased with the effect of FI/BW (Beta  = 0.0199 (±0.004 SE), P<0.0001).The high predictive value of baseline WRA levels for ABA susceptibility was not dependent on a specific genetic background, as was revealed by a repeated single strain exclusion analysis (data not shown). Based on this additional analysis, FI became an non-significant predictor when FVB strain was taken out (Beta  =  −0.0053 (±0.004 SE), P = 0.164). Similarly, when NZW strain was taken out, FI/BW had no longer a significant effect (Beta  =  −0.0066 (±0.004 SE), P = 0.0810). Further, also the FBxW interaction effect was also driven by one strain, CAST strain. When CAST strain was taken out of the analysis FBxW interaction effect was non-significant (Beta  = 0.0059 (±0.003 SE), P = 0.080), indicating that these specific strains dominated the effects of these measures.In the five days of daily scheduled food restriction Wistar WU rats lost weight, however, none of them reached the humane end point criteria (25% body weight loss). Linear mixed models for repeated measures analysis showed that only physical activity measures, WRA and LMA levels, had a significant effect on the level of body weight loss. Among the two, WRA levels had the strongest effect (WRA Beta  =  −0.0242 (±0.004 SE), P<0.0001; LMA Beta  =  −0.0151 (±0.005 SE), P = 0.005). The derived baseline FI/BW had no effect on body weight loss and there was no interaction with baseline WRA or baseline LMA levels.s  =  −0.380, P<0.001; data not shown). Baseline FI was also weakly correlated to baseline WRA levels, but in a positive direction . There was a positive correlation between baseline WRA levels and FI/BW . Baseline WRA and WRA levels on restrictive days (R) 1, 2, 3 and 4 were strongly positively correlated to each other (R1 (n = 98) rs  = 0.885, P<0.0001; R2 (n = 98) rs  = 0.767, P<0.0001; R3 (n = 78) rs  = 0.768, P<0.0001; R4 (n = 64) rs  = 0.593, P<0.0001). s  =  −0.575, P = <0.0001; R2 (n = 98) rs  =  −0.573, P = <0.0001; R3 (n = 78) rs  =  −0.360, P = 0.001; R4 (n  = 64) rs  =  −0.365, P = 0.003; overlay Based on all individual values, baseline BW was not correlated with baseline FI, but was weakly and negatively correlated to baseline WRA levels .Unlike inbred mouse strains, there was a significant but weak correlation between baseline BW and baseline FI . Nor FI either FI/BW were correlated to WRA levels or to LMA levels (data not shown). Baseline BW was also not correlated to WRA levels or to LMA levels (data not shown). There was a positive correlation between baseline levels of WRA and LMA  but were strongly correlated to the other restrictive LMA levels .Baseline LMA levels were weakly correlated to R1 LMA levels  were strongly correlated to the other restrictive days WRA levels .In the present study, using eleven inbred mouse strains, we found that baseline WRA  level was the strongest factor to predict ABA susceptibility when compared to other baseline factors, such as food intake and body weight. In rats, baseline WRA and LMA  levels were the only factors to influence body weight loss. These findings support the notion from retrospective studies in human AN indicating that physical hyperactivity levels may precede anorexia nervosa onset and can also predict the physical activity levels during AN illness In addition, the interaction between physical activity levels and food intake per body weight had initially a substantial effect on ABA susceptibility in the inbred mouse strains. However, further analysis revealed that this effect was mainly driven by the CAST mouse strain which had the highest food intake per body weight levels when compared to the other strains, and was also one of the strains with the highest baseline activity levels. Similarly, the effect of baseline food intake and highest food intake per body weight became non-significant when FVB and NZW strains were taken out of the analysis, respectively. Interestingly, FVB strain had the highest FI levels among the inbred strains and was also one of the strains with high baseline activity levels, while NZW strain had the lowest FI/BW levels. These data suggests that individual mice with high baseline food intake per body weight and high innate physical activity levels have more difficulty to maintain body weight during scheduled food availability than mice that were less active or had lower food intake per body weight under baseline conditions. Translating these mouse findings to human behaviour, they would suggest that patients with high pre-morbid activity levels and high energy requirements are prone to lose weight at a higher rate in the development of illness. Subsequently, with illness progression, the basal metabolic rate decreases and the amount of energy spent with any activity is less than that in normal controls The other baseline factors, such as body weight and food intake, and their derivative food intake per body weight, also exhibited a significant predictor effect on ABA susceptibility, in inbred mouse strains but not in Wistar WU rats. However, in the inbred strains, the magnitude of their predictive value was lower than that of baseline physical activity levels. A closer look at body composition data available in the existing literature, for inbred mouse strains, showed that body fat percentage was similar to the body weight range. The strains with high body weight had also the highest fat percentage, while the rest of the strains had a similar percentage around 20% Although fat percentages were not very different between the strains, circulating leptin levels could be different as leptin release is associated to the fat cell size. Therefore, we were also interested if strain differences in leptin signalling may be related to physical activity levels. While the leptin data for the eleven inbred mouse strains are available only after a fat diet for several weeks The difference that we observe between the outbred rats and inbred mouse strains in ABA susceptibility levels could be attributed to the humane end point criteria, which is reached sooner in mice (15% body weight loss) than in rats (25% body weight loss).Yet, Wistar WU rats lost weight during the restriction days and apart from baseline physical activity levels, the other baseline parameters did not have a significant effect on this body weight loss. The derived FI/BW also did not have an effect and there was no interaction with physical activity data. Interestingly, the present study reveals that the relationship between baseline physical activity levels and level of body weight loss in the ABA model is observed in different rodent species (rats and mice).Taken together, physical activity levels strongly predict ABA susceptibility. The effect of physical activity levels on ABA susceptibility in individual inbred mouse strains is dependent on the genetic background. Baseline body weight and food intake are in general less strong predictors of ABA susceptibility in inbred mouse strains. The effect of body weight and food intake on ABA susceptibility is sometimes strongly dominated by a single strain . Literature search revealed no common metabolic factor that could contribute to body weight loss in inbred strains. While further studies are necessary to unravel the neurobiological mechanisms of these factors and their interrelatedness, these findings and the retrospective AN studies on physical activity levels suggest that pre-morbid physical activity levels of each patient could already be taken into consideration as a possible predictor of illness severity.


Dengue is a serious public health problem worldwide. Dengue shock syndrome (DSS), the severe form of dengue fever, can cause death within 12–24 hours if appropriate treatment is not promptly administered. For patients with DSS and the 30% of non-shocked dengue patients who require intravenous fluid therapy, a range of solutions is available for plasma volume support. Crystalloid solutions, such as normal 0·9% saline or Ringer’s lactate, are the ones most commonly used. In severe cases, colloid solutions may be administered for their greater osmotic effect, although they carry a greater risk of adverse events. This paper summarises the key clinical data, comparing fluid regimens in children with severe dengue, and concludes that the majority of patients with DSS can be treated successfully with isotonic crystalloid solutions. If a colloid is thought necessary, a medium-molecular-weight preparation that combines good initial plasma volume support with good intravascular persistence and an acceptable side-effect profile is optimal. Further research should aim to determine whether there are benefits to early treatment with colloids, and which colloid solution is most effective for resuscitation of DSS patients. Dengue infection causes a broad spectrum of clinical disease, which can range in severity from febrile illness to serious bleeding and shock. Two major pathophysiological responses are associated with severe dengue infection – plasma leakage leading to hypovolaemic shock and/or abnormal haemostasis leading to haemorrhage.1,2The clinical course of dengue includes febrile, critical and recovery phases , and the3During the critical stage, there is an increase in capillary permeability and shock can result if a large volume of plasma is lost through leakage. The recommended regimen for the treatment of DSS is: immediate and rapid replacement of the plasma loss with isotonic crystalloid solutions or, in the case of profound shock, colloid solutions; continued replacement of further plasma losses to maintain effective circulation for 24–48 hours; correction of metabolic and electrolyte disturbances; and blood transfusion in cases with severe bleeding. If large amounts of fluid are required, these should be reduced gradually as plasma leakage decreases in order to prevent hypervolaemia, an excess in plasma volume which can cause oedema, respiratory distress or congestive heart failure, during the recovery stage.1,2Replacement of plasma lost because of increased vascular permeability is a mainstay of severe dengue management, particularly during the critical stage.The most commonly used crystalloid is 0·9%, or ‘normal’ saline, a hypertonic solution with an osmolality of 308 mOsm/L that has higher sodium and chloride levels than normal plasma. Normal saline is a suitable option for initial fluid resuscitation but repeated large volumes of 0·9% saline may lead to hyperchloraemic acidosis and decreased blood pH owing to excessive chloride levels. Therefore, if serum chloride begins to exceed the normal range, other alternatives such as Ringer’s lactate may be preferable.1Ringer’s lactate has lower sodium and chloride contents than 0·9% saline, and an osmolality of 273 mOsm/L. It should be avoided, however, in individuals with liver failure as they have a reduced ability to metabolise lactate.The most common types of colloid used for plasma volume support are gelatin-, dextran- and starch-based solutions , Ringer’s lactate (n = 13), dextran 70 (n = 12) or 3% gelatin (n = 13). In a pooled comparison of crystalloids and colloids, patients who had received colloid infusions had significantly greater increases in mean haematocrit (P = 0·01), blood pressure (P = 0·005), pulse pressure (P = 0·02) and cardiac index (P = 0·02). In individual comparisons, dextran 70 was found to be the most effective solution for improving cardiac index and haematocrit.A number of randomised controlled trials have been undertaken to compare the efficacy of different fluid regimens in managing DSS in children. A pilot study involving 50 children with DSS showed minor differences in the immediate clinical responses to different fluids.n = 51) were compared, fewer differences were found. Of children who received gelatin, significantly fewer had a recovery time of more than 1 hour compared to those who received Ringer’s lactate (P = 0·017). Comparisons between all other solutions were not significant. This study suggests that the majority of patients with DSS have mild-to-moderate shock and will respond well to conventional treatment with crystalloids. A small minority with more serious disease may require more aggressive management with colloids from the outset. However, this study was statistically underpowered and the differences in presenting pulse pressure may have obscured some of the results; for example, any benefit associated with 3% gelatin would be expected to be more pronounced with dextran 70, but there were very few severe patients to compare in the dextran 70 group. Further large-scale studies, stratified for admission pulse pressure, were recommended.A larger study of 230 children with DSS that compared the same four fluids initially suggested improved pulse pressure recovery time following early treatment with colloids.n = 383) were randomised to receive Ringer’s lactate (n = 128), 6% dextran 70 (n = 126) or 6% hydroxyethyl starch (HES) 200/0·5 (n = 129). Ringer’s lactate was found to be as effective as colloid therapy on the primary outcome measure of requirement for colloid rescue or fluid resuscitation. However, patients treated with Ringer’s lactate took longer to achieve cardiovascular stability than patients receiving either colloid (P = 0·02).The largest randomised study to date included two arms, with patients stratified for presenting pulse pressure.n = 67) or HES (n = 62) – and both colloid preparations performed equally well with regard to cardiovascular stability and the number of patients who required resuscitation. However, the dextran preparation was associated with more adverse events than HES, with 8·0 vs 0·5%, respectively, reporting allergic-type reactions including transient high fever and rigors. These adverse events responded to symptomatic treatment alone and there were no differences in severe adverse events such as significant new bleeding or clinical fluid overload.A further 129 children with severe shock (pulse pressure ⩽10 mmHg) were randomised to receive one of the colloids – dextran 70  and 10% HAES-steril (n = 47), for their effectiveness, impact on renal function and haemostasis and any complications.A study of 104 DHF patients with severe plasma leakage who had failed to respond to crystalloids and required fluid resuscitation compared bolus doses of two colloids, 10% dextran 40 (Taken together, these studies show that the majority of children with DSS can be treated successfully with isotonic crystalloid solutions. If a colloid is considered necessary, clinicians must continue to rely on personal experience, familiarity with particular products, local availability and cost. A medium-molecular-weight preparation that combines good initial plasma volume support with good intravascular persistence and an acceptable tolerability profile is probably the optimal choice.It should be noted that the positive overall outcomes described in these studies probably reflect the quality of care as much as the interventions themselves. Hourly observations and immediate access to haematocrit testing coupled with a conservative intervention policy allow fluid requirements to be met as early as possible and carefully titrated. In more poorly resourced settings, intensive care provision is more challenging and complications such as fluid overload are more likely to occur.Further research using higher patient numbers, stratified for severity, will be needed to determine whether early treatment with a colloid confers a true advantage in those with severe shock, and which colloid solution is most effective for resuscitation of DSS patients.


These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS × physical activity interaction effect estimate (interactionP = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts . In secondary analyses, both the FTO rs1121980 (interactionP = 0.003) and the SEC16B rs10913469 (interactionP = 0.025) variants showed evidence of SNP × physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal.Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age et al, PLoS Med. 2010). Although the study by Li et al is widely cited, the original report has not been replicated to our knowledge. Therefore, we sought to confirm or refute the original study's findings in a combined analysis of 111,421 adults. Our analyses yielded a statistically significant interaction effect (interactionP = 0.015), confirming the original study's results; we also identified an interaction between the FTO locus and physical activity (interactionP = 0.003), verifying previous analyses , and we detected a novel interaction between the SEC16B locus and physical activity (interactionP = 0.025). We also examined the power constraints of interaction analyses, thereby demonstrating that sources of within- and between-study heterogeneity and the manner in which data are treated can inhibit the detection of interaction effects in meta-analyses that combine many cohorts with varying characteristics. This suggests that combining many small studies that have measured environmental exposures differently may be relatively inefficient for the detection of gene × environment interactions.We undertook analyses in 111,421 adults of European descent to examine whether physical activity diminishes the genetic risk of obesity predisposed by 12 single nucleotide polymorphisms, as previously reported in a study of 20,000 UK adults (Li  FTO locus et al reported that physical activity offsets the aggregated genetic risk of 12 obesogenic loci Obesity is a major risk factor for many non-communicable diseases including type 2 diabetes, cardiovascular disease, and certain cancers et alIn the current study, we aimed to replicate the findings of Li Supplementary interactionP = 0.015). 2 higher BMI (P = 2.1×10−176), which corresponds to 465 g heavier weight for a person 1.70 m tall. Overall, among physically inactive individuals , each additional BMI-raising allele was associated with 0.186 (SE = 0.006) kg/m2 higher BMI, equivalent to 538 g in weight for a person 1.70 m tall (P = 4.8×10−47), whereas the effect in the most physically active group (CPAI of 4) was 0.143 kg/m2 per GRS allele , or 413 g in weight for a person 1.70 m tall. In the ‘combined active’ group , each additional risk allele was associated with 0.150 kg/m2  higher BMI, or 434 g in weight for a person 1.70 m tall , while the difference in the combined active group was 0.532 kg/m2 .The forest plot in 0 m tall . As illuet alinteractionP = 0.014), but not the former .The CPAI characterizes total physical activity levels by considering both occupational and leisure time physical activity FTO rs1121980 variant, which concurs with previous reports of interaction at this locus SEC16B rs10913469 locus, which has not previously been reported  for FTO rs1121980 and SEC16B rs10913469 variants was −0.052 and −0.049 kg/m2 per risk allele respectively, which compares with βGE of −0.108 kg/m2 per 8.33 alleles for the GRS . For FTO, the interaction effect was almost 10-fold larger in North American than in European cohorts, whereas for the SEC16B locus, the interaction effect was approximately twice the magnitude in North American vs. European cohorts. Supplementary FTO and SEC16B variants from the GRS, the interaction test was no longer statistically significant (in the entire cohort [interactionP = 0.25] or separately within the cohorts from North American [interactionP = 0.39] and Europe [interactionP = 0.44]), strongly suggesting that the GRS × physical activity interaction result is driven by the inclusion of one or both of these variants.In analyses modeling the interaction of each of the 12 individual SNPs and physical activity, two tests of interaction were nominally statistically significant: the reported . It shouet alGE = −0.07 kg/m2 per GRS allele from Li et alet al study GE = −0.07 kg/m2. Although power to detect the interaction effect from the original study is adequate in the current analysis, we observed a much smaller interaction effect estimate in our meta-analysis , which may be owing to the Winner's curseet al, and assuming that these independent variables are not correlated, would require a sample size considerably larger than the current study  when the GRS is dichotomized  and all else is held equal; under this scenario, a sample size of approximately 370,000 observations is required  (Supplementary 2σ = 5.06 to 2σ = 0.25)  to, 4.0, 4.5 and 5.5, when all else is held equal. These analyses . In secondary analyses, we explored whether any of the individual SNP × physical activity interaction tests were statistically significant; of these, the FTO locus (rs1121980) (interactionP = 0.003), consistent with previous findings SEC16B rs10913469 variant yielded statistically significant interaction effects (interactionP = 0.025). The latter finding was not statistically significant after correction for multiple testing, there is no published literature suggesting that this locus is exercise-responsive, and a recent analysis in a randomized clinical trial of lifestyle intervention did not yield evidence of SNP × treatment interactions at the SEC16B rs10913469 locus on weight change phenotypes SEC16B rs10913469 is necessary to confirm or refute its effect-modifying role for physical activity and obesity.Here we sought to replicate a widely cited study in which an interaction on BMI was reported between physical activity and a GRS comprised of 12 obesity-predisposing gene variants Winner's curseWinner's curse differential .It is widely acknowledged that initial reports of genetic association signals are often of considerably greater effect magnitude than yielded by subsequent replication attempts; this phenomenon is termed the GE), the independent variables are expressed on a continuous scale ), ii) the variance in the GRS is large, iii) the GRS and environmental exposure are correlated, and iv) the population variance in the outcome is small, which in part relates to whether exposure and outcome measurements are standardized across studies and measured with reasonable precision  for a given interaction effect size  would likely increase the magnitude of the observed interaction effect for the GRS; however, to achieve this with minimal bias would require further sample collections to validate these new genetic models, which goes beyond the scope of the present study. Nonetheless, we include the relevant information in Although we found statistical evidence of an interaction between physical activity and the GRS in the meta-analysis, it is unlikely that all of the gene variants that comprise the GRS contribute to this interaction effect. For example, the et alet al's study It is also important to highlight that the interaction results reported by Li As a general point, it is important to bear in mind that in observational studies, such as those reported here, marginal and interaction effect estimates may not reflect causal processes. This is because physical activity and obesity correlate with other lifestyle, sociodemographic, and metabolic factors, and the gene variants included in the GRS are unlikely to be functional. Thus, even replicated examples of gene × lifestyle interactions may be confounded by latent variables. Reverse causality is a further concern, particularly with cross-sectional data .et alIn summary, our meta-analysis of 111,421 samples from 11 cohorts of European ancestry yielded results that support those of Li A total of 111,421 participants from the 11 participating cohorts had genotype and phenotype data necessary for the current analyses. Descriptions of the cohorts included in the current analyses are shown in supplementary 2). Obesity was defined according to WHO criteria In most studies, height and weight were measured using wall-mounted stadiometers and calibrated balance-beam scales, respectively , as previously described Information on physical activity was obtained from self-administered questionnaires, which in most instances were validated. Occupational physical activity in most studies was categorized as i) sedentary or standing; ii) light but partly physically active; iii) light and physically active; and iv) sometimes or often physically straining. Leisure time physical activity during the past three months was categorized as exercising: i) occasionally; ii) 1–2 times/week; iii) 2–3 times/week; or iv) >3 times/week. Among leisure-time physical activity (four categories), participants with missing information were given the lowest intensity score, r2>0.8) were genotyped in the 11 cohorts (Supplementary P>0.001). The exception to this was for the SH2B1 rs7498665 SNP in the METSIM and HEALTH2006 cohorts, which did not conform to Hardy Weinberg expectations; sensitivity analyses indicated that removing this SNP from the GRS for the METSIM cohort made no material difference to the overall results (data not shown), and so the results shown here are for the full GRS.DNA was extracted from peripheral blood cells and diluted using standard approaches , missing genotypes were imputed in participants with four or fewer missing values using previously described methods R software (http://www.r-project.org/) and STATA . General linear models (GLM) were used to test the association of the GRS with BMI. Logistic regression was used to test genetic associations with obesity. All analyses were adjusted for age, age2, sex, study center (for multi-center studies), and physical activity (where appropriate), and we assumed additive effects of the alleles. Interaction tests for individual SNPs and the GRS with physical activity (for outcomes BMI or obesity) were performed by including a SNP (or GRS) × physical activity interaction term in the model, with the marginal effect terms also included. The genetic effect estimates for BMI were also calculated by strata of physical activity (i.e. inactive vs. combined active), as described above.Statistical analyses were performed using the SAS software , metan command in STATA . A summary interaction effect estimate was calculated for all 11 cohorts combined using meta-analysis weighted by cohort sample size to summarize the pairwise  interaction coefficients and SE derived from each cohort. Meta-analyses were repeated using random and fixed effects models, but between-study heterogeneity was low ; thus, the results were not materially different to the weighted approach (data not shown), leading us to present only the weighted results here. Analysis of data from the InterAct Study, which includes multiple sub-cohorts, was conducted as described elsewhere Meta-analyses were undertaken using the R code for simulations and power estimation with 1,000 iterations for each sample size simulation. In order to estimate power for different samples sizes, we simulated a 12 SNP GRS using a random normal distribution with mean (s.d.) 11.2 (2.2); physical activity was simulated using a binomial distribution assuming the population prevalence of physical inactivity was 30%, as estimated by Li et al. The approach  was used to simulate different scenarios for the predictor variables: i) with the GRS expressed as a continuous or dichotomized variable (Supplementary 2σ) (GE (Supplementary 2σ) for the population (Supplementary The code-generating program mlPowSim tary σ2) , iii a ret alG) of 0.154 kg/m2 per GRS risk allele and a physical activity marginal effect (βE) of −0.313 kg/m2 (active vs. inactive), physical inactivity prevalence of 30%, and s.d of ±3.5. We assumed that the GRS and physical activity are not correlated and a two-sided critical alpha of 0.05 was used in the calculations. Although the interaction effect estimate (βGE) is not explicitly reported in Li et al's paper, we were able to estimate this from the GRS effect estimates reported in GE∼−0.07) by approximating the difference of βG between the two combined activity categories (active vs. inactive). To accommodate imprecision in the estimation of βGE and the possibility that Li et al's study The main power calculations were performed using estimates obtained from Li Figure S1Forest plot showing the meta-analysis of interaction coefficients  in relation to BMI in the three North American cohorts (a) and the meta-analysis of interaction coefficients  in relation to BMI in the eight European cohorts (b).(TIF)Click here for additional data file.Figure S2GE = −0.013 to −0.10) between a normally distributed genetic risk score  and physical activity (30% inactive and 70% active). Critical alpha = 0.05. All other parameters are taken from Li et al Sample size and power to detect an interaction (β(TIF)Click here for additional data file.Figure S32σ for corresponding prevalence of physical activity, and dotted lines mark the 50th and 80th centile cut-points and the respective sample size requirements for the binary physical activity variable. Power calculations assume a linear interaction effect.Sample size required for 80% power to detect a gene × physical activity interaction in obesity when the prevalence of physical activity (and the variable's variance) varies and all other parameters are fixed. Mean and variance of the genetic risk score are set at 11.2 and 5.06 respectively. Statistical power and critical alpha are fixed at 80% and 0.05 respectively. Solid line represents required sample sizes, dashed line represents (TIF)Click here for additional data file.Material S1Additional details on statistical power simulation.(DOC)Click here for additional data file.Table S1Cohort-specific descriptive statistics.(DOC)Click here for additional data file.Table S2Interactions between the 12 SNPs and CPAI  on BMI across each of the 11 cohorts.(DOC)Click here for additional data file.Table S3Power to detect gene × physical activity interaction in obesity for the different simulation settings: physical activity is a binary variable, and variance of genetic risk score varies.(DOC)Click here for additional data file.Table S4Power to detect a gene × physical activity interaction in obesity for the different simulations settings: physical activity is either binary or approximated by a normal distribution and with different degrees of correlation between the physical activity variable and the genetic risk score.(DOC)Click here for additional data file.Table S5Sample sizes required to detect an interaction between a genetic risk score (12 SNPs) and physical activity (binary) when the standard deviation (S.D.) in the outcome (BMI) varies and all other parameters are fixed.(DOC)Click here for additional data file.Table S6Study description of participating cohorts.(DOC)Click here for additional data file.Table S7Cohort-specific methods used for measuring body mass index and physical activity.(DOC)Click here for additional data file.Table S8Genotyping methods and SNP quality control.(DOC)Click here for additional data file.


Our study employed an optical fiber (OF) methodology  to detect SCN− in a non-invasive and non-destructive manner. Our OF methodology is able to detect trace levels (>3.16 µg L−1) of SCN− in seawater. Given that marine fish exposed to cyanide excrete SCN− in the urine, elevated levels of SCN− present in the seawater holding live reef fish indicate that the surveyed specimens were likely exposed to cyanide. In our study, captive-bred clownfish (Amphiprion clarkii) pulse exposed for 60 s to either 12.5 or 25 mg L−1 of CN− excreted up to 6.96±0.03 and 9.84±0.03 µg L−1 of SCN−, respectively, during the 28 days following exposure. No detectable levels of SCN− were recorded in the water holding control organisms not exposed to CN−, or in synthetic seawater lacking fish. While further research is necessary, our methodology can allow a rapid detection of SCN− in the holding water and can be used as a screening tool to indicate if live reef fish were collected with cyanide.Cyanide fishing is a method employed to capture marine fish alive on coral reefs. They are shipped to markets for human consumption in Southeast Asia, as well as to supply the marine aquarium trade worldwide. Although several techniques can be used to detect cyanide in reef fish, there is still no testing method that can be used to survey the whole supply chain. Most methods for cyanide detection are time-consuming and require the sacrifice of the sampled fish. Thiocyanate anion (SCN Coral reefs currently face an increasing number of anthropogenic threats worldwide  Coral reefs in Indonesia and the Philippines are currently the most at risk (about 95% of existing reefs), due to the use of destructive fishing techniques Cyanide fishing is an inexpensive, highly-effective method used to stun fish as follows: 1) fishermen insert sodium cyanide (NaCN) or potassium cyanide (KCN) tablets into squirt bottles containing seawater; 2) they squirt hydrogen cyanide (HCN) solution into coral crevices sheltering the fish; 3) the cyanide temporarily stuns some of the fish (many die from acute doses); and 4) stunned fish are brought to fishing boats and put into fresh seawater to recover Cyanide-ion selective electrodes (ISE) have been successfully used to identify live reef fish collected with cyanide up to 14 days post-exposure −) to thiocyanate (SCN−), in the presence of a sulfur donor, by the enzyme rhodanese  − and later excreted in the urine The major pathway for cyanide metabolism is the conversion of cyanide  how long after pulse exposure to cyanide do reef fish start to excrete SCN− in detectable levels; and 3) whether fish continue to excrete SCN− in detectable levels at least 4 weeks after their pulse exposure to CN−.The present study sought to determine: 1) if abnormal levels of SCNAmphiprion (one of the two genera of clownfish) are some of the most popular species among marine aquarium hobbyists Amphiprion clarkii , and have attributed these deaths to cyanide poisoning. Although these reports are mostly anecdotal, it was decided to select A. clarkii as the model fish species for the present study.Nearly half of all live reef fish traded by the marine aquarium industry belong to the family Pomacentridae (damselfish) A. clarkii with an average total length  of 40.3± standard error (SE) 3.1 mm, and an average wet weight (± SE) of 1.8±0.2 g were purchased from a local producer , thus assuring that all specimens to be used in this work had never been exposed to cyanide. All specimens were kept for 60 days to acclimate to laboratory conditions in a 270-L glass tank  equipped with an internal circulation pump  and a metal halide lamp  providing a 12 h light:12 h dark photoperiod. The glass tank was connected to a 125-L sump  equipped with a recirculating water pump , a 50 µm mesh bag for mechanical filtration, a Deltec® APF600 protein skimmer, a biological filter of submerged plastic bio-balls and a 300 watt submersible heater (Eheim® Jäger) keeping water temperatures stable at 26±0.5°C. This system was also equipped with an osmoregulator (Reef Set®) used to regulate the water level by replacing evaporated water with freshwater purified by reverse osmosis to keep salinity at 35 g L−1 . The fish maintenance system used seawater prepared by mixing RO water with a synthetic salt mix . Water quality parameters were monitored every other day using colorimetric tests  with the exception of pH, which was monitored using a Pinpoint® pH meter (PH 370 by American Marine) and remained within optimal ranges for A. clarkii . All fish were fed daily to satiation with a commercial pelleted food (Hikari® Marine S).Twenty-seven cultured specimens of A. clarkii were randomly divided into 3 groups of 9 fish for cyanide pulse exposure. The first group was used as a control (no exposure to cyanide), while the second group was exposed to a nominal concentration of 12.5 mg L−1 of CN−, and the third group was exposed to a nominal concentration of 25.0 mg L−1 of CN−. The pulse exposure to CN− was performed in three steps: 1) exposure bath; 2) first cleaning bath; and 3) second cleaning bath. The exposure bath was performed as follows: all fish from the same group were collected with a hand-net and dipped for 60 s into a 15-L tank filled with: a) synthetic seawater with no cyanide (group 1); b) synthetic seawater dosed with CN− at a concentration of 12.5 mg L−1 (group 2); and c) synthetic seawater dosed with CN− at a concentration of 25.0 mg L−1 (group 3). After the pulse exposure to cyanide all fish from the same group were dipped for 60 s into a 20-L tank filled with synthetic seawater with no cyanide (first cleaning bath). This procedure was repeated one more time for all fish from all groups  was selected according to the work by Hanawa et al. − initially selected for the pulse exposure to this poison in the present study were also those used by Hanawa et al. −1). However, since preliminary experiments revealed that at 25.0 mg L−1 of CN− some fish mortality occurred after an exposure of 60 s, it was decided to select 12.5 and 25.0 mg L−1 (rather than 25.0 and 50.0 mg L−1) of CN− as the experimental concentrations to be tested during pulse exposure.After the acclimation period (60 days), the 27 −1 of NaCN , with a CN− concentration of 10.00 g L−1, was prepared by dissolving 5.65 g NaCN in 300 mL deionized (Milli Q) water. The experimental concentrations of CN− employed during the pulse exposure (12.5 and 25.0 mg L−1) were prepared by adding 18.7 mL and 37.5 mL of the stock solution to 15 L of synthetic seawater, respectively.Two different chemicals are commonly employed in cyanide fishing: sodium cyanide (NaCN) and potassium cyanide (KCN). As both chemicals are potent poisons, it has been assumed that their fish-stunning capacity does not differ significantly −, all fish were randomly distributed into 2-L glass jars  filled with 1.5 L of synthetic seawater for post-exposure depuration  water samples were collected in order to determine any background levels of SCN−.Following the pulse exposure to CN− levels were performed in the 2-L glass jars employed to hold the fish in order to ensure that no false positives were recorded. Additionally, a preliminary study confirmed that no retention of SCN− occurred in the 2-L glass jars after repeated exposure to this compound over a period of 4 weeks (the same duration as the depuration period employed in our study).Time-zero measurements of SCN− present in the water samples collected during the experimental period were determined using a new methodology based on optical fiber (OF) detection coupled to a liquid chromatography system  and seawater samples (20 µL collected from each 2-L jar employed for stocking fish during the depuration period) were introduced by a micro-syringe  at the top of the injector unit of our OF apparatus. After separation on the column the analytes reached the analytical tube containing the coated OF sensor to generate an analytical signal. Calibration models were built by injecting 20 µL of different concentrations  of standard solutions. The concentration of SCN− was determined by direct interpolation into the calibration curve within the linear dynamic range, with detection limits being calculated by using the following formula: y = yB+3 sB, where sB is the standard deviation (SD) of the blank signal estimated as sy/x, the residual SD taken from the calibration line, and yB is the blank signal estimated from the intercept also taken from the calibration line. The accuracy recorded for our OF methodology was high, as the error recorded /expected value)×100) was lower than 0.4%. This new technique based on OF detection coupled to a liquid chromatography system displays a high analytical performance, both in terms of linear range (4–400 µg L−1) and detection limits (3.16 µg L−1), an analytical time of less than 6 min, and is comparable to methodologies employing high performance liquid chromatography with UV detector (HPLC-UV) The concentration of SCNstem see  [34]. St− in the water samples collected during the experiment (28 days) were analyzed using a repeated measurements ANOVA, with the concentration of cyanide employed during the pulse exposure being used as the categorical factor. Statistical analyses were performed using the software STATISTICA version 8.0 (StatSoft Inc.), with the assumptions of normality and homogeneity of variance checked prior to analysis through the Shapiro-Wilks and Levene test, respectively. Mauchly's test of sphericity was used to determine if the variances of the differences between all combinations of related groups (levels) are equal. Whenever significance was accepted, at p<0.05, the Tukey multiple comparison test was used for pairwise comparison of means The existence of significant differences in the levels of SCNThis study was carried out in strict accordance with the recommendations present in the Guide for the Care and Use of Laboratory Animals of the European Union – in Portugal represented by the Decreto Lei n°129/92 de 06 de Julho, Portaria n°1005/92 de 23 de Outubro de 1992. Approval by a named review board institution or ethics committee was not necessary as the final model for ethical experimentation using fish as biological models was not implemented in Portuguese research units at the time of experimentation. This work was conducted under an institutional license for animal experimentation and a personal license to first author Marcela Vaz, and to the co-authors Isabel Lopes and Ruth Pereira, issued by the Direcção Geral de Veterinária (DGV), Portuguese Ministry of Agriculture, Rural Development and Fisheries.− at both tested concentrations all fish displayed frantic swimming and strong gasping behavior, followed by a loss of balance, cessation of swimming (specimens rested motionless at the bottom of the container), and finally a complete loss of respiratory activity after about 30 and 50 s of the beginning of the pulse exposure with 12.5 and 25.0 mg L−1 of CN−, respectively. One and two specimens, respectively, of A. clarkii exposed to 12.5 and 25.0 mg L−1 of CN− did not recover from anesthesia and died within 30 minutes following pulse exposure. Apart from a more agitated swimming behavior due to the netting during the 3 steps of the pulse exposure, fish in the control group did not display any of these responses and no mortality was recorded.During the pulse exposure to CNA. clarkii died during the depuration period at day 18 post-exposure (a fish previously exposed to 25.0 mg L−1 of CN−). The total number of A. clarkii reaching the end of the depuration process (28 days) were 9, 8 and 6 specimens, respectively, for the control group, and the groups pulse-exposed to 12.5 mg L−1 or 25.0 mg L−1 of CN−.A single specimen of −1) of SCN− were recorded in the water samples collected from the jars holding fish from the control group (no exposure to CN−)  of 4.95±0.04 µg L−1 of SCN− was already detected in the water holding A. clarkii pulse-exposed to 25.0 mg L−1 of CN−. Concerning A. clarkii pulse-exposed to 12.5 mg L−1 of CN−, SCN− was only detected 6 days after exposure (3.84±0.04 µg L−1). At that time, water samples collected from specimens pulse-exposed to 25.0 mg L−1 of CN− displayed a significantly higher content of SCN− (7.25±0.05 µg L−1) (p<0.0001) . When the experiment ended (28 DPE), SCN− levels in the water holding fish pulse-exposed to 12.5 and 25.0 mg L−1 of CN− remained nearly identical to those recorded at 21 DPE (− (>3.16 µg L−1) were recorded in the samples collected from the batches of newly prepared synthetic seawater.During the whole depuration process no detectable levels (>3.16 µg L to CN−) . On the <0.0001) . By the t 21 DPE . No dete− in seawater samples collected from containers holding fish previously pulse-exposed to CN−.This non-invasive and non-destructive approach, with an analytical time of less than 6 min − by fish previously exposed to CN−. The data demonstrate how SCN− excretion varied according to the initial concentration of CN− during pulse exposure, as well as how long SCN− was detectable after water samples were collected for analysis. While detectable levels of SCN− started to be excreted by fish the day following pulse-exposure to 25.0 mg L−1 of CN−, it took nearly one week for fish that had been exposed to a lower concentration of CN− (12.5 mg L−1) to start excreting detectable levels of SCN−. Other aspects that may influence the excretion of SCN− by fish exposed to cyanide are: 1) the duration of the exposure period, 2) fish species and size, 3) collection and post-collection handling stress, 4) the maintenance time and husbandry practices of exporting or importing facilities, and 5) shipping duration − on apprehended squirt bottles employed for cyanide fishing (ranging from 760 to more than 2000 mg L−1) − in detectable levels by our FO methodology.The present work confirms that our OF methodology can be used to determine elevated concentrations of SCN− pulse exposure, as a concentration and exposure time that promoted no mortality in Dascyllus aruanus (25.0 mg L−1 of CN− and 60 s) A. clarkii specimens with slightly smaller size in the present study. If such physiological variability can occur among members of the same family . By keeping cyanide-caught fish for longer periods in their facilities wholesalers and/or retailers also risk losing fish due to their high mortality − in seawater holding tanks as cyanide-caught fish would be held during the excretion period of SCN−.By increasing the holding period in export facilities of fishes beyond the end-point of SCN− levels higher −.Traders acting in good faith and willing to avoid potential false positives should export their live reef fish in synthetic seawater and not in natural seawater. The main reason for employing this practice is that natural seawater can display SCN−, rather than CN−); 2) delivers results much faster than current techniques employed for testing total CN−, which are time-consuming and labor intensive; 3) is non-invasive and non-destructive (no need for taking fish muscle or blood samples), and does not require the sacrifice or a heavy manipulation of the fish; and 4) is easier, safer and cheaper to use than current techniques available to detect live reef fish collected illegally using cyanide. This approach can be used to screen live reef fish immediately upon arrival to importing countries. In importing countries it is easier to take legal action against traders landing cyanide-caught fish. Authorities of importing countries can also provide information to the exporting countries regarding which exporters supply fish illegally collected with cyanide. This strategy may discourage importing enterprises from dealing with unreliable suppliers and shift the legal pressure from impoverished fishermen to those truly profiting from this illegal and destructive activity.In conclusion, our OF methodology: 1) tests for the compound that is excreted in the urine by fishes poisoned with cyanide (SCNFile S1−) in seawater.PDF file of the manuscript describing in detail the methodology employed in this work to determine thiocyanate (SCN(PDF)Click here for additional data file.


Diffusion tensor imaging (DTI) based fiber tractography (FT) is the most popular approach for investigating white matter tracts in vivo, despite its inability to reconstruct fiber pathways in regions with “crossing fibers.” Recently, constrained spherical deconvolution (CSD) has been developed to mitigate the adverse effects of “crossing fibers” on DTI based FT. Notwithstanding the methodological benefit, the clinical relevance of CSD based FT for the assessment of white matter abnormalities remains unclear. In this work, we evaluated the applicability of a hybrid framework, in which CSD based FT is combined with conventional DTI metrics to assess white matter abnormalities in 25 patients with early Alzheimer’s disease. Both CSD and DTI based FT were used to reconstruct two white matter tracts: one with regions of “crossing fibers,” i.e., the superior longitudinal fasciculus (SLF) and one which contains only one fiber orientation, i.e. the midsagittal section of the corpus callosum (CC). The DTI metrics, fractional anisotropy (FA) and mean diffusivity (MD), obtained from these tracts were related to memory function. Our results show that in the tract with “crossing fibers” the relation between FA/MD and memory was stronger with CSD than with DTI based FT. By contrast, in the fiber bundle where one fiber population predominates, the relation between FA/MD and memory was comparable between both tractography methods. Importantly, these associations were most pronounced after adjustment for the planar diffusion coefficient, a measure reflecting the degree of fiber organization complexity. These findings indicate that compared to conventionally applied DTI based FT, CSD based FT combined with DTI metrics can increase the sensitivity to detect functionally significant white matter abnormalities in tracts with complex white matter architecture. Diffusion tensor imaging (DTI) based fiber tractography (FT) is currently the most widely used method to reconstruct fiber pathways in the brain, despite its well known limitations in regions with complex white matter architecture In the past decade, several advanced approaches for characterizing the intra-voxel diffusion profile have been developed to overcome the limitations of the second-rank diffusion tensor model We hypothesize that if CSD based FT is more accurate in reconstructing fiber bundle trajectories in regions with “crossing fibers”, it should be more sensitive to microstructural abnormalities underlying cognitive dysfunction than DTI based FT in these tracts. In tracts without “crossing fiber” regions, both methods should perform equally. To test this hypothesis we used a hybrid framework, in which CSD based FT is combined with conventional DTI metrics to assess white matter abnormalities in patients with early Alzheimer’s disease (AD). This allowed us to examine the microstructural properties of specific white matter pathways in relation to memory performance, while overcoming the well-known limitations of DTI based FT in regions with “crossing fibers”. We evaluated this CSD-DTI framework for two white matter tracts: one specifically selected because it contains many regions of “crossing fibers”, i.e. the superior longitudinal fasciculus (SLF) and one with only one fiber orientation, i.e. the midsagittal section of the corpus callosum (CC). Diffusion measures in these tracts have been previously shown to be altered in patients with AD compared to controls using tract based analyses In this paper, we examined whether CSD based FT combined with DTI metrics can increase the sensitivity to detect functionally significant white matter abnormalities in tracts with complex white matter architecture compared to conventionally applied DTI based FT.The medical ethics committee for research in humans of the University Medical Center Utrecht, the Netherlands has approved this research. Informed written consent was obtained from all participants. All clinical investigation has been conducted according to the principles expressed in the Declaration of Helsinki. Exclusion criteria were a history of stroke in the last 2 years, a history of stroke with subsequent cognitive deterioration, schizophrenia or other psychotic disorders, major depression, alcohol abuse, brain tumor, epilepsy or encephalitis. Incapacitated patients with a severe stage of AD, indicated by a clinical dementia rating score >1 Twenty five patients , 19 with early stage AD and 6 with amnestic mild cognitive impairment (a-MCI) were recruited via a memory clinic at the University Medical Center Utrecht. Probable or possible AD was diagnosed according to the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer Disease and Related Disorders Association (NINCDS-ADRDA) criteria 3, 2.5 mm slice thickness (without gap), 48 slices, repetition time 6638 ms, echo time 73 ms, flip angle 90 degree, acquisition matrix 88×88 (in plane resolution of 2.5 mm) and reconstructed at 128×128, 45 isotropically distributed diffusion-sensitizing gradients with a b-value of 1200 s/mm2, and one b = 0 s/mm2 image 2 image was on average 33.5 with a standard deviation of 11.7 MRI data were collected using a Philips 3.0 Tesla scanner . Diffusion MRI data were obtained using a single-shot spin echo EPI sequence with the following parameters: field of view = 220×220×120 mm2 image with ElastixThe DTI data sets were corrected for eddy current induced geometric distortions and subject motion by realigning the diffusion-weighted images (DWIs) to the b = 0 s/mmExploreDTI software package (www.exploredti.com). We reconstructed the SLF and the CC using both FT methods with a uniform seed point resolution of 2 mm3 and a maximum deflection angle of 30 degrees. For the DTI based FT an FA threshold of 0.2 was applied. Analogously, the applied termination threshold for CSD based FT was a fiber orientation distribution (FOD) value of 0.1 (the harmonic degree of the estimated FOD coefficients was limited to 6) Standard deterministic streamline DTI The SLF, including SLF II, III and the arcuate fasciculus The CC was reconstructed as described previously 2−λ3/λ1) Diffusion parameters: fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (DR), axial diffusivity (DA), and the normalized planar diffusion coefficient  Differences in configurational tract characteristics  after DTI and CSD based FT were analyzed with paired-samples T-test. The composite memory score and diffusion measures were all normally distributed. The relation between mean DTI metrics  and memory performance was evaluated using linear regression analysis adjusted for age, sex, and level of education. Differences in the relation between DTI metrics and memory obtained with DTI- versus CSD-based FT was calculated using Steiger’s Z-statistic for dependent correlations Because crossing fibers affect the tensor estimation To examine the possibility that the relation between diffusion measures and memory performance is affected by tract volume 3) CSD: 19.88±5.25; DTI: 10.10±2.78; p<0.001). In 75% of the patients the tract length was longer with CSD compared to DTI based FT (mean tract length ± SD (mm) CSD: 109.2±10.9; DTI: 98.3±13.6; p<0.001). The approximate tract volume of the CC segment was also larger for all patients with CSD compared to DTI based FT (mean tract volume ± SD (cm3) CSD: 9.59±0.98; DTI: 7.03±0.94; p<0.001).For the SLF, lower FA values of the SLF were associated with worse memory performance for both FT methods, but this association was only statistically significant for CSD based FT (standardized regression coefficient (95% CI) DTI: 0.39 ; p = 0.054, CSD: 0.41 ; p = 0.042) , model 1We also assessed the relation between diffusion parameters and cognitive performance in a tract without “crossing fibers”: the midsagittal segment of the CC. The FA of the CC was not significantly associated with memory performance with either tractography method, whereas a trend was observed for an association between memory and mean MD ; p = 0.051, CSD: −0.37 ; p = 0.074) , model 1This is the first report on the application of CSD based FT to detect white matter abnormalities in patients with (early) AD. Our results indicate that 1) CSD based FT in combination with DTI metrics significantly increased the sensitivity to detect a relation between white matter abnormalities and memory performance in a tract with “crossing fibers” (SLF); and 2) the relation between DTI metrics and memory was comparable between both FT methods in a tract without “crossing fibers” .In line with our expectations, fibers of the SLF were more likely to terminate in regions with “crossing fibers” with DTI-based FT. By contrast, with CSD based FT the SLF continued beyond these crossings to more temporal and dorsal frontal regions, which is in line with descriptions from autopsy studies increased mode of anisotropy in patients with MCI compared to controls only in areas where the SLF intersects the projection pathways The adverse effects of “crossing fibers” on the interpretation of diffusion measures such as MD and FA have been previously demonstrated e.g. , but theVoxels with “crossing fibers” are more likely to be included with CSD based FT. We therefore used the planar diffusion coefficient as a covariate to overcome the confounding effects of these “crossing fibers” on the diffusion metrics in relation to cognition. If two fiber populations within a voxel “cross” or “kiss”, the shape of the diffusion tensor becomes more planar (disc-like). As a result, a voxel with intact crossing fibers can have a similar FA value compared to a voxel with a degenerating non-crossing fiber population. However, the planar diffusion coefficient between these voxels will be different The effects of DTI metrics on cognitive performance were more prominent for DR than for DA, suggesting that the observed association is more likely driven by myelodegeneration than by a loss of axonal integrity Our study has a number of limitations. One is the modest sample size, which may have decreased our sensitivity to detect a relation between structure and function. Still, we were able to replicate previously observed associations between diffusion measures and AD severity Finally, the use of the planar diffusion coefficient as a quantitative measure to characterize “crossing fibers” may be valid in cases were two fiber bundles intersect or overlap, but may not be directly applicable in regions where three or more fiber bundles intersect. Although previous work reported that no more than two fiber populations could be observed in the SLF Since DTI based FT fails in regions with “crossing fibers”, more accurate methods to characterize the microstructural properties of fiber pathways are in need. Here we showed that CSD based FT combined with standard DTI metrics increases the sensitivity to detect functionally significant white matter abnormalities in a tract with “crossing fibers” in patients with early AD compared to DTI based FT. The use of a hybrid CSD-DTI framework is therefore a promising tool to detect functionally significant white matter changes in regions with complex white matter architecture.Figure S1Adjusted correlations between diffusion parameters of the SLF and memory performance. Top row: the relation between FA/MD and memory with DTI based fiber tractography. Bottom row: the relation between FA/MD and memory with CSD based fiber tractography. FA/MD and memory are expressed as standardized residuals after adjusting for age, sex, level of education, and the planar diffusion coefficient Click here for additional data file.Figure S2Unadjusted correlations between diffusion parameters and memory performance. Top row: the raw FA values of the SLF (left) and medial segment of the CC (right) with DTI based fiber tractography. Bottom row: the FA of the SLF (left) and medial segment of the CC (right) with CSD based fiber tractography.(TIF)Click here for additional data file.