Soil amendments, such as straw mulch, organic fertilizers and superabsorbent polymer (SAP), are extensively applied to improve soil structure and porosity, and we reported the functional consequences of the individual application of these amendments in our previous study. However, whether combined amendments are more effective than their individual applications for improving soil pore structure is unknown. Here, we conducted X-ray computed tomography (CT) scanning on undisturbed soil columns to investigate the efficiency of two-amendment application, including straw mulch and organic manure, SAP and organic manure, or SAP and straw mulch, for improving soil pore properties and pore distribution. The X-ray CT technique allows us to accurately determine the number, morphology, and location of macropores (>1 mm in diameter) and smaller pores (0.13–1.0 mm). Compared to the control treatment, which showed the lowest increase in soil porosity, all the combined treatments led to an increase in the numbers of both macropores and smaller soil pores, causing a significant improvement in soil structure and porosity. Among these treatments, the application of both straw mulch and organic manure was the most effective for improving soil porosity and soil physical structure. For example, Antonio et al.3 performed a three-year experiment and found that straw mulch can improve several soil structure properties, including soil bulk density and porosity, aggregate stability, effective water content and conductivity. Similarly, Liu et al.4 reported that no tillage in combination with straw mulch can improve the subsoil structure, where an increase in the equivalent diameter (derived from measurements of soil water suction) of aeration pores was observed at a soil depth of 10–20 cm. However, a reduction in the equivalent aperture of invalid pores was also found. Adding organic matter  to soil can both elevate the organic carbon content6 and promote the formation of more and larger aggregates7, thereby enforcing the soil aggregate stability and water storage capacity10. Additionally, SAP are widely applied to crop fields as a typical soil amendment to enhance aggregate formation11 and improve the soil physical characteristics, such as soil structure, pore properties12, capillary porosity, total soil porosity13 and soil infiltration14. Moreover, the application of SAP performed the additional function of inhibiting the soil crust formation and soil surface evaporation.Soil structure amendments, such as straw mulch, organic manure, and superabsorbent polymer (SAP), have been widely applied to improve soil organic matter, fertility15. CT scanning has been applied to the study of soil pore properties17 and pore distribution20. This technique has also been introduced to accurately measure the number, size, and location of macropores (>1 mm in diameter)21. CT scanning has been successfully applied in measurements of the distribution, number, shape and connectivity of macropores in longitudinal and transverse cross-sections of both undisturbed and packed soil columns22. The quantification of pore structure at mm to µm scales has been shown to be important in predicting micro-scale fluid flow properties23. Furthermore, the approach is used to quantify the pore distribution of reconstructed soils during ecological restoration in opencast coal mines24.Computed tomography (CT) scanning is increasingly used as a non-destructive imaging technique for the high-resolution  characterization and quantification of soil physical properties25, and soil pore morphology affects the transmission and preservation of soil moisture. In soils, irregularly shaped pores usually retain moisture better than circular pores, but the latter can transport water more efficiently. In other words, high soil pore circularity is beneficial for the transport and conservation of water in the soil, which can enhance water utilization and ensure that plants uptake sufficient water. Previous studies have highlighted the influences of pore size on soil water movements. For instance, Lv et al.26 suggested that pore connectivity is better to facilitate the transmission of water from the pores when the soil pore diameter is less than 0.99 mm. Luxmoore et al.27 indicated that macropores (>1 mm) promoted rapid soil water movement through the movement of air and water within the soil profile, which is consistent with the findings reported by Perret et al.28 and Fox et al.29. Thus, understanding the effects of these soil amendments on soil pore structure is important for the development of effective soil and water conservation and management practices. Soil pore structure is evaluated based on morphological characteristics , the distribution of pore spaces, and interactions between connectivity and the spatial pore distribution22. Our previous study indicated that when soil amendments are used alone, such as straw mulch, SAPs and organic manure, they can significantly improve the characteristics of soil pores and pore distribution in soils30. However, little information exists on the changes in soil porosity in response to the combined application of soil amendments, such as straw mulch in combination with organic manure (straw mulch + organic manure), SAP + organic manure, or SAP + straw mulch. The quantitative evaluation of such combined treatments is required to understand their effects on the characteristics and distribution of soil pores. In this study, we hypothesized that the combined application of soil amendments would improve soil porosity by altering pore characteristics within the soil profile. The objectives of this study were to (1) evaluate differences in the CT-measured characteristics  of >1.0 mm pores, 0.13–1.0 mm pores and ≥0.13 mm pores  among treatments and (2) determine whether an observed correlation exists between CT-measured pore parameters and conventional cutting ring approach-derived characteristics .Soil pore structure plays a key role in the movement of water in both topsoil and subsoil, which is closely related to soil surface runoff and permeabilityZea may L.), where the hydrolytic nitrogen (N), available phosphorus (P) and available potassium (K) contents were 55.9, 15.9 and 69.4 mg·kg−1, respectively, and topsoil organic matter and total N were 11.9 and 0.85 g·kg−1, respectively.Soil columns were excavated from a field site in Tongxu County, Henan, China . The study area has a flat topography and 682.4 mm of average annual rainfall. The sandy alluvial soil in this region is derived from river alluvium, with a mechanical composition of 67.8% sand (2–0.02 mm), 18.1% silt (0.02–0.002 mm) and 14.1% clay (<0.002 mm). The field was previously uniformly fertilized and used for growing corn , organic manure (chicken manure composted for one year), and straw mulch (treated approximately 5 months before the experiment started) were selected as soil amendments in this study. Combinations of two soil amendments were applied to the research fields. A randomized complete block design was used with three replications. The field experiment was performed in wheat-maize rotation croplands from October 2009 to June 2013. The treatments included i) the control treatment ; ii) straw mulch (SM)  combined with the application of organic manure (OM) , designated SM + OM; iii) the combined application of SAP (60.0 kg ha−1) and organic manure (SAP + OM); and iv) the application of SAP amended with straw mulch (SAP + SM), in which the tillage depth was 15 cm during the winter growing season. For the soil background, land history, and soil tillage pattern, the control is consistent with the other amendments treatments. Prior to sowing, all fields received a base fertilizer of ordinary superphosphate (90.0 kg ha−1 P2O5) and N (112.5 kg ha−1) and were also supplied with additional N fertilizer at the jointing (45 kg ha−1) and grain filling stages (67.5 kg ha−1). The assigned fertilizer levels were set with reference to local field managements.Based on the results of our previous studyIn June 2013, after the wheat harvest, undisturbed soil columns (for the determination of soil pores by CT scanning), ring cut samples  and undisturbed soil samples (for the determination of water-stable aggregate content) were collected sequentially from the topmost soil (0–13 cm).−3. Next, the steel rods were then carefully pulled out before the beginning of CT scanning31.Undisturbed soil columns were obtained using rigid PVC pipes . Three replicates of each soil column were collected from each sampling site and then stored at 4 °C until further analysis. In order to determine the threshold of pore, two soil columns with known pore diameter were firstly developed. Macropores of a known size were obtained by placing two steel rods (2.0 and 2.4 mm in diameter) in separate PVC tubes. The tubes were filled with soil , then was used to measure and ensure the pore threshold under the same bulk density of 1.25 g cm22. The colour image density of the smaller region was black, while the greater regional colour density was lighter.A medical 256-speed Discovery ST16 PET/CT scanner  was operated to scan soil columns, with a peak voltage current of 120 kV, scanning set of 110 mA and 1 s scanning time. The scanning thickness was 1 mm. Scan depths were set at 25 mm up to 120 mm from the top of the soil column. The scanning thickness was 1 mm, and CT images at 5 mm increments were used for the further analysis of soil pore structure properties. Thus, 20 cross-sectional images, which were two-dimensional image, were obtained for each of the 12 soil columns. A total of 240 CT scan images (20 images × 4 treatments × 3 replicates) were captured. The scanning image brightness represented different soil density areas, and the soil macropores could be clearly displayed from the image32. Then, the images were converted to 8-bit images and segmented using a threshold value of 56, which was selected on the basis of CT scans of two cores with a known size of macropores33. First, we calculated the macro-pore size (macropores of a known size from two soil columns with known pore diameter) based on image analysis results using ImageJ by assuming one threshold value for the threshold sample; then, we compared the obtained size to the actual size. If the difference between the sizes was too large, then we selected another threshold value until the difference was reduced to less than 1%33. Segmentation was used to convert the greyscale images into black and white images, which can be used to differentiate between the soil matrix (white areas) and soil pores (black areas). These images were analyzed to determine the pore number, area and perimeter length. The pixel resolution was 0.13 × 0.13 mm . The X-ray beam width or “slice” thickness was 1 mm, producing a volume element (voxel) size of 0.017 mm3. Therefore, soil pores with equivalent pore diameters (≥0.13 mm), based on two-dimensional image  were reliably identified using this image-processing method. The three-dimensional visualization of soil pore networks in the soil columns was reconstructed via saturated volume rendering using Amira software. In this study, we just used a schematic diagram of 3D view of soil pore structures via one direction to roughly evaluate the effects of different treatments on soil pore properties.The captured CT scanning data were recorded as greyscale images in PNG format with a size of 50 × 50 mm. These images were processed and analyzed using ImageJ software according to Abramoff’s protocol27 and soil pores with an equivalent diameter of 0.13–1.0 mm30. Thus, the pore numbers were counted as the number of >1.0 mm pores and 0.13–1 mm pores in a CT image. Pore circularity represents the degree of the shape of the macropores, and higher values of circularity indicate that soil pores are more circular34. Porosity was calculated as the area of the image covered by >1.0 mm pores (for >1.0 mm porosity) or 0.13–1 mm pores (for 0.13–1.0 mm porosity) and expressed as a percentage. Pores >0.13 mm (for >0.13 mm porosity) were defined as the sum of >1.0 mm pores (for >1.0 mm porosity) and 0.13–1 mm pores (for 0.13–1.0 mm porosity). Pore circularity was calculated using the following equation:C is circularity and ranges between 0 and 1, A is the pore area (mm2), and L is the pore perimeter length (mm).Soil pores based on two-dimensional image were divided into two size classes: >1.0 mm pores35, soil bulk density36, total porosity, non-active porosity, capillary porosity and aeration porosity were measured using the cutting ring method. The water-stable aggregate content was estimated using the wet sieving method37. More details about these methods can be found in the Supporting Information.The sampling depth of the soil samples was 0–13 cm. Field moisture capacityP < 0.05” and “P < 0.01” level. The Pearson correlation coefficient was calculated to assess the relationships between the soil pore parameters and soil characteristics.Each experiment was conducted in triplicate. The data was presented as mean value ± standard deviation (SD) and analysed by one-way analysis of variance (ANOVA) with the least significant difference (LSD) test using SPSS Statistics Version 10.0 at “SM + OM treatment possessed more numerous soil pores and size distribution, clearly showing the general higher soil porosity than other treatments  in the treated groups relative to the control treatment  treatments. For the 0.13–1.0 mm pores, the SM + OM treatment showed the highest circularity, followed by the SAP + SM, SAP + OM, and control treatments. Overall, the combined application of the SM + OM treatment presented the best performance in terms of soil pore circularity.In general, pore morphology affects the movement of air and moisture through the soil. Thus, we compared the pore circularity among the treatments, which describes the morphology of a soil pore relative to a circle with a perfectly circular shape . For any given pore area, the circularity was reduced as the irregularity of the pore perimeter increased. High pore circularity facilitates water transport in the soil with the increased accessibility. In soil columns among all treatments, the circularity of 0.13–1.0 mm pores was higher than both >0.13 mm soil pores and >1.0 mm pores  was thus provided by the SM + OM treatment. Consequently, the increases in pore circularity will benefit the soil infiltration capacity as well as the transport and exchange of water and gas.The highest degree of soil pore circularity was observed in the 0.13–1.0 mm pore size class, while the lowest degree was found in >1.0 mm pores Fig. . For allSM + OM treatment showed the lowest soil bulk density, while the SAP + SM and SAP + OM treatments contributed to intermediate soil bulk densities. Compared to the control treatment, all the combined treatments showed significantly higher levels of field capacities  < SAP + SM (35.8%) < SM + OM (53.8%). Given the data from the standard cutting ring approach, the total porosity, capillary porosity and non-active porosity were the highest in the SM + OM treatment, followed by the SAP + SM and SAP + OM treatments, but notably low levels of aeration porosity were found in all the combined treatments compared to the control , which had a stronger correlation than pore number (P < 0.05) among all sizes of soil pores (Table P < 0.05). Bulk density showed strong negative correlations with all three pore parameters (P < 0.05 or P < 0.01), apart from soil porosity in 0.13 mm pores and >1.0 mm pores, which had a weakly negative correlation. For circularity, >0.13 mm pores (P < 0.01) exhibited a highly significant negative correlation with bulk density compared to >1.0 mm pores or 0.13–1.0 mm pores (P < 0.05). For porosity, there was also only a significant negative correlation between the porosity in 0.13–1.0 mm pores and bulk density (P < 0.05). Given the data from the cutting ring approach, the level of total porosity showed a strong positive correlation with pore numbers in >0.13 mm pores and 0.13–1.0 mm pores, porosity in 0.13–1.0 mm pores, and circularity in all sizes of soil pores (P < 0.05), the data of which were obtained from the CT scanning technique. A similar correlation pattern was found between the capillary porosity and these soil pore parameters. Non-active porosity levels had a significantly positive correlation with the circularity in >0.13 mm pores and >1.0 mm pores (P < 0.05) and a notably positive correlation in the 0.13–1.0 mm pore size (P < 0.01). In contrast, aeration porosity had a negative correlation with these pore parameters derived from CT scanning, in which the circularity of 0.13–1.0 mm pores had the most significant effect (P < 0.01), followed by >0.13 mm pores and >1.0 mm pores (P < 0.05).Field capacity as well as water-stable aggregate contents showed a significant positive correlation with circularity  exerted the greatest effect on the improvement of soil pore structure, providing the greatest capacity to potentiate plant growth. As noted by Acharya et al.39, straw mulch is widely applied to increase soil humus content and promote soil particle aggregation, leading to a more effective improvement of the soil pore structure. Other recent studies have suggested that both straw mulch and organic matter can increase soil organic carbon (SOC), which greatly affects the soil pore distribution38. This finding is also consistent with a study by Schlüter et al.40 that reported that higher levels of SOC had a distinctive effect on the quality of the soil structure. Moreover, organic manure was suggested to improve the soil cluster structure through the elevation of soil organic carbon, humus content and aggregates42. Muhmmad et al.43 observed a more aggregated structure in the soils where both animal manure and inorganic fertilizer (NPK) had been applied compared to soils fertilized with animal manure alone. In comparison to inorganic fertilization, organic fertilization showed that the soil physical quality can be improved at different scales44, as reflected by the increases in pore numbers and porosity30. Furthermore, the long-term application of organic manure can improve the macro-pore to secondary pore ratio, which reduces the proportions of smaller pores.Soil pore structure exerts a profound influence on water movement between topsoil and subsoil, which is closely related to surface runoff and soil permeabilityet al.45 investigated the effects of mulching pattern on soil physical properties and crop yield in a wheat-maize cropping system and found that straw mulch can enhance soil porosity and reduce the ratio of capillary to non-capillary porosity. Our data are also in agreement with previous reports showing that SM + OM treatment caused a higher increase in pore numbers and porosity levels at a soil depth of 20–60 mm than in other treatments. The soil pore numbers and porosity in combined amendment treatments were still higher than those of the control, although the beneficial effects on soil structure were attenuated with increased soil depths.In addition to the size and quantity of soil pores, porosity is another important factor affecting the transmission and preservation of soil moisture. Zhao SM + OM and SAP + OM treatments was better than in the other treatments. Deurer et al.46 analyzed the effects of the long-term application of organic carbon on apple orchards using X-ray microtomography and reported that organic carbon affected the soil structure, which had significantly greater macroporosity than in conventionally managed soils. Moreover, SAP was applied to trigger large soil aggregate formation11. Our previous studies supported the role of SAP application in improving the soil structure and pore properties via the shrinking/swelling process, agglutination of the soil particles, and reduced infiltration14. Furthermore, our results suggested that the increase in water-stable aggregation may improve the soil physical properties and make soils more suitable for crop growth, augmenting the soil total porosity, capillary porosity, non-active porosity and soil field capacity and diminishing the soil bulk density and aeration porosity. The cutting ring method-derived properties  were distinctly positively correlated with soil pore number, pore circularity and porosity of 0.13–1.0 mm soil pores measured via the X-ray CT technique, which highlights the importance of soil pore structure for soil moisture content. The soil bulk density and aeration porosity were significantly negatively correlated with the cutting ring method-measured parameters, indicating that bulk density or aeration porosity was linked to both the number and morphology of soil pores. Taken together, these findings show that the macroscopic physical characteristics can be reliably represented for the micro-scale physics of soil pore structure, which indicates the potential for translating micro-scale soil processes to macro-scale soil properties. In addition, the quantification of pore structure is more effective in predicting fluid flow properties23. Luo et al.47 found that X-ray CT-derived macroporosity and path numbers are important to effectively predict saturated hydraulic conductivity. Thus, quantitative relationships between X-ray CT-derived soil pore geometry characteristics and flow and transport parameters require further investigation.In addition, soil pore shape has been well documented to play an important role in the transmission and preservation of soil moisture. When the pore shape is more irregular, the water transport process in soil pores is slower than circular pores. Hence, maintaining higher soil pore circularity is critical for soil water transportation and conservation. Our study showed that the improvement of the pore morphology in different soil layers as well as the soil pore circularity in the In this study, we combined the X-ray CT scanning of undisturbed soil columns with standard cutting ring measurements to investigate how the combination of two amendments affects soil pore properties and pores distribution. The results clearly reveal that the application of two soil amendments was more effective than a single amendment in the improvement of soil pore parameters, represented as pore number, porosity and circularity. Our results obtained from cutting ring approaches suggest that the application of a combination of two soil amendments is a good strategy to synergize the effects of organic matter to improve the field capacity, total porosity, capillary porosity and non-active porosity and reduce the bulk density and aeration porosity. Correlation analysis results indicate the potential for translating micro-scale soil processes to macro-scale soil properties. The findings show the immense potential of linking X-ray CT-derived soil pore parameters with classical soil physical measurements for quantifying soil structure and functions. Across all treatments in this study, the combined application of straw mulch and organic manure led to the best overall improvement in soil structure and porosity. Further efforts should focus on the development of high-resolution X-ray CT or micro-CT for the investigation of soil physical structure and should consider improvements in the sample size, image contrast and resolution.Supporting Information


The present study examines this effect by manipulating the types of premises and conclusions  at an electrophysiological level using a semantic category-based induction task. With regard to behavioural results, higher inductive strength was found in typical conclusions in all premise conditions, whereas a longer response time for atypical conclusions was only found in general and typical premise conditions. The ERP results had different response patterns: in the general premise condition, a larger P2, as well as a smaller P3 and LPC (500–600 ms), were elicited by atypical conclusions relative to typical ones; in the typical premise condition, a larger P2 and LPC (600–700 ms) were found for atypical conclusions; in the atypical premise condition, however, only a larger P2 was found for atypical conclusions. The divergent evidence for the typicality effect indicated that the processing of the typicality effect in general, and specific premise conditions, might involve different cognitive processes, such as resource allocation and inference violation, which yielded new insights into the neural underpinnings of the typicality effect in a category-based induction.Behavioural studies have indicated that semantic typicality influences processing time and accuracy during the performance of inductive reasoning ( Furthermore, typicality has a significant effect on processing time and accuracy during object naming, learning, categorisation, and reasoning12368The ability to use semantic concepts representing semantic categories to name, learn, classify, and reason is crucial in our daily lives, however, not all members of a category are of equal importance, and certain categories members , or whether it did not. They found that a larger P2 and smaller N400 were elicited by typical members relative to atypical members in inclusion, whereas typical members elicited a larger N2 than atypical members in the exclusionA similar typicality effect was found in other tasks, such as category-based deduction, semantic categorisation, and lexical decision61213et al.1617et al.In fact, Lei et al.et al.19Based on Lei As mentioned above, previous studies revealed that N1, P2, N2, P3, and N400 mean amplitudes or peak-latencies were sensitive to the typicality effect. Indeed, N1 was sensitive to selective attention, which influenced the further processing of perceptual features21232426et al.Furthermore, previous studies have shown that the N2 effect was modulated by perceptual mismatching, which has been extended into a reasoning task282923313219343536c. 600 ms) was modulated by different factors in both EEG and MEG studies, such as mismatched conclusion or property violations193544Moreover, late ERP components, such as N400 and LPC, were also found for the processing of typical effect or category based-induction. Previous studies have found that the N400 effect was sensitive to the strength of semantic relations3839Based on the aforementioned considerations, the main purpose of this study was to explore how types of premises modulated the typicality effect in category-based induction at behavioural and electrophysiological levels. To achieve this, three types of premises and conclusions  are manipulated. In the general premise condition, participants reason from basic level category to general, typical or atypical members of the category . In the typical premise condition, they reason from typical premises to general, typical or atypical members . In the atypical premise condition, participants reason from atypical premises to general, typical or atypical conclusions . Participants are required to assume the that information about the premise is true , and to assess whether, or not, the information about the conclusion  is plausible, and make a “strong” or “not strong” response, which has been used extensively17The key focus here was on the electrophysiological data elicited by the conclusion (s), upon which certain judgments could be made. Based on previous studies, short RTs and higher “strong” responses should be found for typical conclusions relative to atypical conclusions1610113547e.g., Bird X; → Bird/sparrow/penguin X?), so if the information about the premises is true, the information about the conclusions is also true. For example, if all birds have property X is true, sparrows must have property X, because a sparrow is a type of bird; however, in the typical/atypical premise condition, the information about the conclusions is not necessarily true, but only has a “strong” or “not strong” plausibility. For example, if all sparrows have property X is true, all birds/penguins do not necessarily have property X. Furthermore, different response patterns should also be found in typical and atypical premises, because typical premises are more representative than atypical members, especially when the conclusion is a general category. As results, although early ERP effects related to the perceptual and attention processes for typical and atypical conclusions should be found in different premise conditions , the late ERP effects related to premise-conclusion matching or categorisation violations should have different response patterns .More importantly, the types of premises should have a critical regulatory role in the typicality effect when exploring the ERP effects elicited by the conclusions, especially for general premises and specific  premises. In the general premise condition, specifically, the category in the premise includes, or is identical to, the category in the conclusion  between the ages of 19 and 24 were paid for participating in the main experiment. Participants that initially rated the stimuli did not participate in the main procedure. All participants were healthy and right-handed with normal or corrected-to-normal vision, and gave their informed written consent before the experiment. All experimental protocols were approved by the University’s ethics committee , and the methods were carried out in accordance with the relevant guidelines and regulations.i.e., birds, fishes, insects, vegetables, flowers, and fruits) and four artificial categories  were selected as stimuli. In a manner similar to previous studies6Before the main experiment, six natural categories  and atypical members , t (58) = 21.66, p < 0.001.After this, another 40 undergraduates participated in a typicality test, in which participants were required to estimate the extent to which these members epitomised the category on a seven-point scale, where 7 indicated the highest typicality. For example, category members such as “sparrow” and “crow” received a typical rating of “6” or “7” as birds. As a bird, however, the degree of typicality for category members such as “penguin” or “duck” was lower than that for “sparrow” and “crow”, and they might receive a rating of “3” or “4” as birds. After these studies, five typical members and five atypical members were selected from each category, plus these categories themselves, composed a set of 110 categories. An independent e.g., Sparrow A, which meant that sparrows have property A; see e.g., Premise: Crows have property A; Conclusion: Sparrows have property A) was used to induce a memory load1e.g., A) was used and never any actual properties as clarified further elsewhere19The premise and conclusion of the arguments consisted of one of the above selected 110 categories or members See , as wellAs shown in plausible.In the main procedure, participants judged whether the argument was “strong” or “not strong” by pressing one of two keys. The order of the stimuli within the task was randomised and counterbalanced. Following previous studies1718e.g., Apple X) indicating that the category member had that property, was presented for 1000 ms, followed by a blank screen shown for a random duration (800–1000 ms). Next, the conclusion and a novel property ended in “?”  linked by a blank space appeared and remained until participants made a response. Participants were instructed to respond as rapidly and accurately as possible to the conclusion, and make a “strong” or “not strong” response by pressing “F” or “J” with the left or right index finger, which was counterbalanced across subjects. The next trial began after the presentation of a blank screen for 500 ms.The categories and category members were presented through one to three black Chinese characters, which appeared on a grey background as generated by the E-prime software. As shown in i.e., A = A, T = T) served as filler trials in which participants had to make “strong” responses, which was compared with the responses to control condition (“not strong”), to improve the efficacy of the designTo ensure that each participant understood the instructions, they were asked to repeat the instructions in their own words. Furthermore, to reduce the effect of familiarity, all categories and their members were presented by category at the beginning of experiment. The procedure was divided into practice and test phases. The tests phases consisted of 510 trials under the main experimental conditions  and 240 trails for baseline conditions . The identical condition . The ground electrode was on the medial frontal line and references were on the left and right mastoid52M = 0.99, SD = 0.02), but made a very low response for the control condition . However, including these conditions in the same analysis diluted any possible effect of typicality on induction. Indeed, we compared these identical conditions, and no significant ACC and RTs, as well as any ERP effects, were found among them.In a manner similar to previous studies617F ratio were corrected for violations of the sphericity assumption based on Greenhouse Geisser, and Bonferroni corrections were used for each comparison.Based on previous studies and the topographies, we focused our analysis on the mean peak latency, peak amplitude, and the mean amplitude between 50 ms and 150 ms (N1), 150 ms and 250 ms (P2), 250 ms and 350 ms (N2), 350 ms and 450 ms (P3), as well as the mean amplitude of LPC . Four-way repeated measures ANOVAs were performed for each time window, with premise , conclusion , laterality  and frontality  as repeated factors. For all analyses, the degrees of freedom of the F  = 7.12, p = 0.014, η2 = 0.34. Similarly, there was significant main effect on the conclusion, F  = 8.82, p = 0.002, η2 = 0.39. In both situations, the proportion for atypical conditions  was lower than general  and typical conditions , whereas no significant difference was found between general and typical premises (p > 0.26). However, the interaction between premise and conclusion was not significant, F  = 1.45, p = 0.25, η2 = 0.09.Two-way repeated measures ANOVAs were performed for the proportion of “strong” responses and reaction times (RTs), with premise  and conclusion  as repeated factors. The main effect of premise on “strong” responses was significant, F  = 22.60, p < 0.001, η2 = 0.62. Similarly, there was significant main effect on the conclusion, F  = 56.98, p < 0.001, η2 = 0.80. In both situations, RTs in the atypical conditions were longest , smallest in the general conditions , and intermediate in the typical conditions , which differed from each other for the three conditions (ps < 0.006). Furthermore, the interaction between premise and conclusion was significant, F  = 3.33, p = 0.025, η2 = 0.19. Pair-wise comparison indicated that the RT for a general conclusion was shorter than typical and atypical conclusions in general, typical and atypical conditions (ps < 0.006). However, a shorter RT was found for typical conclusions relative to atypical conclusions only when the premise was of the general category (p < 0.001) and typical members (p < 0.05), but not for atypical premise members (p > 0.90).The main effect of premise on RT was significant, As mentioned above, four-way repeated measures ANOVAs were performed for the mean peak amplitude, mean amplitude, and peak latency of N1, P2, N2, and P3, as well as the mean amplitude of LPC see  and 3.p = 0.016). The main effects of frontality and laterality on N1 mean amplitude were significant. Pair-wise comparison indicated the N1 component in frontal, frontal central and central sites was larger than central-parietal and parietal sites (ps < 0.05). The N1 at left and middle sites was larger than that at right sites (ps < 0.01). The interactions between laterality and premise on N1 mean amplitude and peak were significant. Pair-wise comparison indicated that the difference between a general premise and an atypical premise was found at middle and right sites (ps < 0.04), but not at left sites (p > 0.10). Although three way interaction among frontality, premise, and conclusion on the N1 amplitude was found, no significant interaction between premise and conclusion was found at sites with different frontality (ps > 0.10). No other significant difference was found.As shown in ps < 0.01), and the mean amplitude for general conclusions was smaller than that for atypical conclusions (p < 0.001). The interaction between premise and conclusion on P2 latency was significant, the P2 latency for general conclusions was shorter than that for atypical conclusion in the atypical premise condition (p = 0.03), but not in general and typical premise conditions (ps > 0.10).The main effects of conclusion on P2 mean amplitude and peak were significant. Pair-wise comparison found that the P2 peak elicited by general conclusion was smaller than typical and atypical conclusions (p = 0.03). Furthermore, the interaction between frontality and conclusion was significant. In addition to the larger P2 amplitude for atypical members relative to the general category found at all sites (ps < 0.04), the P2 elicited by atypical members was larger than that arising from typical members at frontal, frontal central, and central sites (ps < 0.05), but not at central parietal and parietal sites (ps > 0.33). Similarly, the interaction among frontality, laterality, and conclusion on P2 amplitude was significant. Further analysis indicated that a smaller P2 was elicited by general relative to atypical conclusions at Fz, F3, F4, FCz, FC3, FC4, and Pz (ps < 0.007), whereas such a difference between general and typical conclusions was only found at F3 sites (p = 0.013). Furthermore, the larger P2 elicited by atypical conclusions relative to typical conclusions was only found at F4 and FC4 (ps < 0.05). No other significant difference was found (ps > 0.05).The main effects of laterality on P2 mean amplitude and peak were significant. The P2 at right sites was larger than that at left sites (ps < 0.05). Similarly, the N2 elicited by typical and atypical conclusions were larger than general conclusion (ps < 0.02). The interaction between premise and conclusion on N2 mean amplitude was significant. The N2 elicited by a general conclusion was smaller than typical and atypical conclusions only in general premise conditions (ps < 0.01), but not in typical and atypical conditions (ps > 0.20). The interaction among frontality, premise, and conclusion on N2 mean amplitude was significant. Further analysis indicated that the smaller N2 elicited by a general conclusion relative to typical and atypical conclusions was only found at frontal, frontal central, and central sites (ps < 0.01). The three way interaction among laterality, premise, and conclusion on N2 mean amplitude and peak was significant. Further analysis found that the interaction between premise and conclusion was only found at middle sites (ps < 0.01), but not at left and right sites (ps > 0.10).The main effects of premise, as well as conclusion, on N2 peak and mean amplitude were significant. Pair-wise comparison indicted the N2 elicited by typical premise was larger than general premise (ps < 0.05), and the N2 at left and middle sites were larger than that at right sites (ps < 0.001). The interaction between laterality and premise on N2 mean amplitude was significant. Pair-wise comparison indicated that the N2 elicited by a typical premise was larger than that of a general premise at middle (p = 0.03) and right sites (p = 0.01), but not at left sites (p = 0.15). Furthermore, the interactions between laterality and conclusion on N2 mean amplitude and peak were significant. That is, the N2 elicited by general conclusions was smaller than that from typical and atypical conclusions at middle and right sites (ps < 0.03), but not at left sites (ps > 0.24). The interaction among frontality, laterality, and conclusion was significant. Further analysis indicated that the difference between general and typical conclusions was found at Fz, F4, FCz, FC4, Cz, C4, CPz, CP4, Pz, and P4, whereas the difference between general and atypical conclusions was found at Fz, FCz, FC4, Cz, C4, CPz, CP4, Pz, P4, and P3 (ps < 0.05).The main effects of frontality and laterality on N2 mean amplitude and peak were significant. The N2 component in frontal, frontal central sites was larger than central-parietal and parietal sites (p < 0.05). Similarly, the P3 elicited by general conclusions was larger than typical and atypical conclusions (ps < 0.03). The interactions between premise and conclusion on P3 mean amplitude and peak were significant. Although the P3 elicited by a general conclusion was larger than that from typical conclusion in general and typical premise conditions (ps < 0.05), a larger P3 elicited by general conclusion relative to atypical conclusions was found in general and atypical premise conditions (ps < 0.05). Furthermore, the smaller P3 amplitude elicited by atypical conclusions relative to general and typical conclusions was only found in general premise conditions (ps < 0.02). The three interactions among frontality, premise, and conclusion on P3 amplitude and peak were significant. Further analysis indicated that the smaller P3 elicited by atypical conclusions relative to general (ps < 0.01) and typical conclusions (ps < 0.05) was only found in general premise conditions at frontal, frontal central, central, and central parietal sites. The three way interaction among laterality, premise, and conclusion on P3 amplitude was significant. Further analysis found that the interaction between premise and conclusion was more significant at middle (p = 0.001) and right sites (p = 0.003) relative to left sites (p = 0.03). No other significant difference was found.The main effects of premise, as well as conclusion, on P3 peak and mean amplitude were significant. Pair-wise comparison indicted the P3 elicited by a general premise was larger than that from a typical premise (ps < 0.01), and the P3 at right sites was larger than left and middle sites (ps < 0.01). The interaction between laterality and premise on P3 amplitude was significant. Although the P3 elicited by a general premise was larger than that arising from a typical premise at all sites (p < 0.05), it was only larger than that arising from an atypical premise at middle sites (p = 0.025). Furthermore, the interactions between laterality and conclusion on P3 amplitude and peak were significant. That is, the P3 elicited by a general conclusion was larger than that from an atypical conclusions at all sites (ps < 0.03), whereas it was larger than that arising from typical conclusions only at middle and right sites (ps < 0.05). The three way interaction among frontality, laterality, and conclusion on P3 amplitude was significant. Further analysis indicated that the difference between general and typical conclusions was found at F4, FC4, C4, CPz, CP4, and P3, whereas the difference between general and atypical conclusions was found at Fz, F4, FCz, FC4, Cz, C4, CPz, CP4, and Pz (ps < 0.05).The main effects of frontality and laterality on P3 amplitude and peak were significant. The P3 in central, central-parietal, and parietal sites was larger than that at frontal, frontal central sites (ps < 0.03), whereas it was just larger than that from an atypical premise at 700–800 ms (p = 0.042). Furthermore, the interaction between premise and conclusion was significant at 500–700 ms, but not at 700–800 ms. At 500–600 ms, the mean amplitude of LPC elicited by atypical conclusion was smaller than that from a typical conclusion only in general premise condition (p < 0.03), but not in typical and atypical premise conditions (p > 0.50). At 600–700 ms, however, only larger LPC for atypical conclusion relative to typical conclusion was found in typical premise conditions.The main effect of premise on LPC was significant, pair-wise comparison found that the LPC elicited by a general premise was larger than that arising from typical and atypical premises at both 500–600 ms and 600–700 ms (ps < 0.05). In addition, the LPC at right sites was larger than that at left and middle sites (ps < 0.01). Furthermore, the interaction between laterality and premise, as well as laterality and conclusion, was significant at 500–600 ms and 600–700 ms. The larger LPC elicited by general premise relative to typical and atypical premises was found in middle and right sites (p < 0.05), but not in left sites. Although the absolute difference among conclusions was larger at right sites relative to middle and left sites, they did not reach a significant level. The interaction between frontality and conclusion was significant at 600–700 ms and 700–800 ms. Further analysis indicated that the LPC elicited by atypical conclusions was larger than that from typical conclusions at parietal sites (p = 0.03). The three way interaction among frontality, laterality, and conclusion was significant. Further analysis indicated that the difference between general and atypical conclusions was found at F4 and FC4 (ps < 0.05), whereas the difference between typical and atypical conclusions was only found at P4 (ps < 0.01). No other difference was found.The main effect of frontality, as well as laterality, was significant at 500–800 ms. The LPC component in central, central-parietal, and parietal sites was larger than frontal, and frontal central, sites , while ostrich is an atypical item because of its less intercorrelated features . In the present study, there are more common attributes between the premises and typical conclusions relative to atypical conclusions, resulting in higher inductive strength for typical items. These results were consistent with previous studies1The main goal of this study was to examine how types of premises modulate the typicality effect in category-based induction by focusing on the electrophysiological data elicited by the conclusions. The behaviour data showed that the processing of arguments with typical conclusions was faster than that of arguments with atypical ones in both general and typical premise conditions, but not in atypical premise conditions. Furthermore, the “strong” rate for arguments with typical conclusions was higher than that for atypical ones under all premise conditions. According to recent accounts48et al.e.g., the argument such as “robins secrete uric acid crystals, therefore, birds secrete uric acid crystals” was considered more convincing than the argument that “robins secrete uric acid crystals, therefore, ostriches secrete uric acid crystals”).Furthermore, the “strong” rate for arguments with general conclusions was higher than atypical conclusions. A smaller P2 and N2, as well as a larger P3 and LPC at 500–600 ms, were elicited by general conclusions relative to atypical conclusions. These results are consistent with the research about the inclusion fallacy, in which generalisation for general conclusions is considered stronger than that for atypical conclusions in the specific premise conditions1The typicality effect was also present in the ERP data. As shown in et al.ps > 0.23).Previous studies found a larger N1 effect for atypical stimuli relative to typical stimuli61212et al.232426e.g., attention processes increased with the organisational semantic demand), and elicited a larger P2 amplitude, just like the larger P2 elicited by Chinese characters with low combinability and consistency in orthographic and phonological processing26Similar to the findings of a previous studyOf greater interest was the observation of smaller P3 effect for atypical conclusion relative to typical conclusion in general premise conditions. As mention earlier, the P3 wave reflected the information-processing cascade related to attentional and memory mechanisms2331e.g., similarity) were involved in specific premise conditions, which reduced the effect of expectation or increased task difficulty, reducing the sensitivity of P3 thereto.A related explanation was that the difference in P3 amplitude just reflected the satisfaction of expectations19343536571More importantly, the LPC at 500–600 ms elicited by atypical conclusion was smaller than that arising from general and typical conclusions in general premise conditions, whereas the LPC at 600–700 ms elicited by atypical conclusion was larger than that for typical conclusions in typical premise conditions. Furthermore, no significant difference in LPC effect was found in atypical premise conditions. As mentioned earlier, LPC in a reasoning study reflected mismatched conclusions1935In the general premise conditions, one plausible view was that the LPC effect among different conclusions reflected the old/new effect, as well as the contextual effect42In the typical premise condition, however, no item was repeated and the context of typical members could not lead to this difference. Similar to the findings of previous studies3522e.g., similarity and coverage) might be involved in the specific and general premise conditions, resulting in different LPC effects60e.g., LPC). Future research into this issue was deemed necessary.The divergence of the LPC effect between typical and atypical conclusion in general and typical premise conditions might be caused by reasoning processing and the validity thereof. As mentioned earlier, the information about the conclusions in the general premise condition must be true, whereas in the typical premise condition they were not necessarily true. Furthermore, different processes .Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


To examine influences of adiposity from early adolescence to early 20s on cardiovascular disease (CVD) risk in the multiethnic Determinants of young Adult Social well-being and Health (DASH) longitudinal study.In 2002–2003, 6643 11–13-year-olds from 51 London schools participated at baseline, and 4785 were seen again at 14–16 years. Recently, 665 (97% of invited) participated in pilot follow-up at 21–23 years, with biological and psychosocial measures and blood biomarkers (only at 21–23 years). Regression models examined interplay between ethnicity, adiposity and CVD.At 21–23 years, ∼30–40% were overweight. About half of the sample had completed a degree with little ethnic variation despite more socioeconomic disadvantage in adolescence among ethnic minorities. Regardless of ethnicity, overweight increased more steeply between 14–16 years and 21–23 years than between 11–13 years and 14–16 years. More overweight among Black Caribbean and Black African females, lower systolic blood pressure (sBP) among Indian females and Pakistani/Bangladeshi males compared with White UK peers, persisted from 11–13 years. At 21–23 years, glycated haemoglobin (HbA1c) was higher among Black Caribbean females, total cholesterol higher and high-density lipoprotein (HDL) cholesterol lower among Pakistani/Bangladeshis. Overweight was associated with a ∼+2 mm Hg rise in sBP between 11–13 years and 21–23 years. Adiposity measures at 11–13 years were related to allostatic load , HbA1c and HDL cholesterol at 21–23 years. Ethnic patterns in CVD biomarkers remained after adjustments.Adolescent adiposity posed significant risks at 21–23 years, a period in the lifespan generally ignored in cardiovascular studies, when ethnic/gender variations in CVD are already apparent. Determinants of young Adult Social well-being and Health (DASH) is one of the few cohorts with an ethnically diverse composition in their 20s, a time when physical health is at its peak, yet when early signs of disease begin to appear.DASH has high retention rates and provides a comprehensive picture of the ethnic patterning of growth from early adolescence.It is the first UK study, as yet from a pilot follow-up, to report a prospective association between adiposity measures and cardiovascular disease risk from early adolescence to early adulthood in an ethnically diverse cohort.The lack of data before age 11 years, blood biomarkers only at 21–23 years and pilot sample size at 21–23 years are limitations.The UK has a rapidly growing ethnically diverse population. In the 2011 Census, the non-White British population in the UK was 20% of the population and 55% in London. The public health implications are significant given the well-documented ethnic disparities in cardiometabolic disease. People of South Asian or Black African descent have, respectively, fourfold and threefold elevated risks of diabetes compared with White Europeans.Obesity, and its metabolic and haemodynamic consequences, underpins a significant proportion of adult ill health. The Foresight Obesity project forecasted that by 2050, 60% of men and 50% of women will be obese, with growing ethnic disparities projected. The impact of obesity acquired during rapid somatic growth on cardiometabolic disease is unclear, and the tracking of obesity from adolescence into early adulthood may account better for ethnic differences in disease. In the pilot follow-up of our Determinants of young Adult Social well-being and Health (DASH) study, we used an initial panel of biomarkers, traditional and novel, neither often measured at this age. Lipid profiles and glycosylated haemoglobin (HbA1c by which type 2 diabetes can now be defined) were included. Inflammation in adipose tissue is likely to play a key role in vascular damage preceding overt diabetes.11There are few cohorts with an ethnically diverse composition in early adulthood, a time when physical health is at its peak, yet when early signs of disease begin to appear. Studies that tracked growth from childhood and collected biomarkers and vascular measures are mainly of White Europeans with few in Europe varying in ethnic make-up.Details of the DASH study can be found elsewhere.Written informed consent was obtained from all participants. Ethnicity was self-reported, checked against reported parental ethnicity and grandparents' country of birth. Bangladeshis and Pakistanis were combined due to small sample sizes.Field staff were trained for 1 week prior to the start of fieldwork, and were recertified at 6-monthly intervals. Equipment was calibrated regularly by the field supervisors. During adolescence, assessments were conducted in schools and at 21–23 years, in community locations .2, obese a BMI of ≥30 kg/m2. Waist circumference (cm) was measured midway between the 10th rib and the top of the iliac crest, and 0.5 cm subtracted to correct for measurement over T-shirt or vest. The mean of two duplicate measures was derived for the WHtR.Body Mass Index (BMI) can underestimate body fat among South Asians and overestimate among African-origin populations.2; WHtR male 0.89, female 0.86; TC 4.8 mmol/L; HDL cholesterol 1.8 mmol/L; HbA1c 37.0 mmol/mol. AL score was derived by summing the points.BP was measured using validated OMRON M5–1 instruments and appropriately sized cuffs, after sitting quietly for a timed 5 min, with >1 min between three subsequent readings. The mean of the second and third readings was used in analysis. A 25 mL blood sample was taken by venepuncture at 21–23 years. We examined the following seven biomarkers: systolic blood pressure (sBP), diastolic blood pressure (dBP), WHtR, BMI, TC, HDL cholesterol and HbA1c. These biomarkers were used to create AL, based on the highest risk quartile values of the sample distribution.A self-complete questionnaire measured smoking, SEC .Continuous variables  did not require transformation as the Shapiro-Wilk test indicated normal distributions. All models were gender-stratified. We first examined potential effects of age (used as single years) and ethnicity, adjusted for each other, on outcomes. Ethnic-specific effects were tested using interaction terms ethnicity × age and ethnicity × adiposity. Measures of adiposity  were added to the core model with age and ethnicity, using separate models for each adiposity measure. Models were gender-stratified. Social correlates  were added to the models only if they were statistically significant in univariable analyses and where the log likelihood ratios indicated better fitted models.Mixed-effects models examined longitudinal outcomes for sBP and adiposity measures . Linear regression models were used for HbA1c, HDL, TC and AL. Two sets of models were run. The first set was with adiposity measures at 11–13 years to examine whether, at this critical period of adolescence, there was an association with biomarkers at 21–23 years. The second investigated the influence of change in adiposity measures between 11–13 years and 21–23 years. Stata V.13 was used for all analyses. Missing data among continuous variables were <5% for BP and anthropometric variables (N=664 for mixed-effects models with BP as outcome) and 24% for blood biomarkers (N=502 for linear regression models). Missing data for categorical covariates were coded and included in the analyses. Statistical significance was considered at p<0.05.10.1136/bmjopen-2016-013221.supp1supplementary dataAt 11–13 years, Black Caribbeans and Black Africans were 2–3 cm taller, Indians ∼5 cm shorter and Pakistanis/Bangladeshis ∼2 cm shorter than White UK. At 14–16 and 21–23 years, however, the White UK–ethnic minority difference was evident only for South Asians, who remained shorter  risk from early adolescence to early adulthood in an ethnically diverse cohort. At 11–13 years, 30% of the follow-up sample was overweight/obese compared with 39% at 21–23 years, with a larger increase between 14–16 years and 21–23 years than between 11–13 years and 14–16 years. There was a striking steady increase in smoking from early adolescence. Ethnic patterns in BP and vascular biomarkers varied by gender, and were unaltered after adjustment for adiposity or social factors. Of note was that from early adolescence, Black Caribbeans and Black Africans were more likely to be overweight than their White UK peers, with Black Caribbean females also having a higher HbA1c at 21–23 years. Regardless of ethnicity, adiposity measures were related to sBP at each age, and adiposity measures at 11–13 years were related to the AL, HbA1c and HDL at 21–23 years.Growth acceleration for ethnic minorities from a young age may have had some impact. The Millennium Cohort Study showed accelerated growth from birth for ethnic minority children who were not preterm or small for dates.20Childhood growth acceleration is associated with later obesity and CVD, via mechanisms including rises in BP, types of fat deposition as well as perhaps insulin resistance. Differential growth and metabolic factors between birth and 3 years of age between South Asians and Europeans suggest insulin levels change after those of adiposity, as also shown by adiponectin concentrations.The ethnic-specific blood biomarker profiles suggest changing lipid profiles among Black Caribbeans. Earlier studies of adult migrant Caribbeans,352) were as follows—White UK 6.41 (95% CI 5.50 to 7.32), Black Caribbeans 9.13 (95% CI 7.80 to 10.46), Black Africans 8.01 (95% CI 7.02 to 9.00), Indians 7.71 (95% CI 6.67 to 8.74) and Pakistanis/Bangladeshis 7.43 (95% CI 6.43 to 8.42). The small sample size of the ethnic groups prohibited robust testing of ethnic-specific effects. The primary aims of the pilot follow-up were to locate the diverse groups in the cohort, investigate whether they would participate in a subsequent follow-up study and agree for their parents to be invited to join DASH, and whether they would consent to the different measures, notably the biomarkers. Missing data were low for anthropometry and BP but relatively high for the blood biomarkers which could have introduced bias. Despite these small numbers, these findings in a small age range provide a robust platform for planning future studies.DASH contains a diverse sample, has high retention rates and low item-non-response, mainly due to enormous community support. The lack of data before age 11 years, and blood biomarkers only at 21–23 years, lack of body composition measures at age 11 years, and the small sample sizes for the ethnic groups in the pilot follow-up are limitations. Prior to DASH, a large-scale study with an explicit focus on ethnicity had not been attempted. Our pilot studies in 2001 suggested that response rates would be low if bloods were taken and starting at younger ages would incur a high loss to follow-up between primary and secondary schools. Measuring adiposity in ethnic minorities is not unproblematic but whether ethnic-specific bioelectrical impedance analysis calibration is required continues to be debated.A recent stabilisation of obesity rates in children in England is good news but there is no room for complacency. Early adolescent overweight adversely affected CV health of 20-year-olds and overweight increased between late teens and early 20s, regardless of gender or ethnicity. Physical peak is expected in the 20s, a transitional age that is ignored in interventions. Urgent attention is required to prevent diminishing ‘peak’ health in a ‘coming of age’ generation that faces the additional challenges from economic precarity due to the economic recession.


MDDG) is a genetically and clinically heterogeneous group of muscular disorders, characterized by congenital muscular dystrophy or later‐onset limb‐girdle muscular dystrophy accompanied by brain and ocular abnormalities, resulting from aberrant alpha‐dystroglycan glycosylation. Exome sequencing and Sanger sequencing were performed on a six‐generation consanguineous Han Chinese family, members of which had autosomal recessive MDDG. Compound heterozygous mutations, c.1338+1G>A (p.H415Kfs*3) and c.1457G>C , in the protein O‐mannosyltransferase 1 gene (POMT1), were identified as the genetic cause. Patients that exhibited milder MDDG manifested as later‐onset progressive proximal pelvic, shoulder girdle and limb muscle weakness, joint contractures, mental retardation and elevated creatine kinase, without structural brain or ocular abnormalities, were further genetically diagnosed as MDDGC1. The POMT1 gene splice‐site mutation (c.1338+1G>A) which leads to exon 13 skipping and results in a truncated protein may contribute to a severe phenotype, while the allelic missense mutation (p.W486S) may reduce MDDG severity. These findings may expand phenotype and mutation spectrum of the POMT1 gene. Clinical diagnosis supplemented with molecular screening may result in more accurate diagnoses of, prognoses for, and improved genetic counselling for this disease.Muscular dystrophy‐dystroglycanopathy ( POMT1, OMIM 607423) are related to autosomal recessive muscular dystrophies, which include three subtypes, designated as MDDGA1 (OMIM 236670), MDDGB1 (OMIM 613155) and MDDGC1  POMT1 gene, including a previously described splice‐site mutation (c.1338+1G>A) involving in splicing of precursor mRNA, and a missense mutation  in a highly conserved region, were detected to be the possible genetic aetiology of MDDGC1 in this family.Muscular dystrophy‐dystroglycanopathy (MDDG), a common group of muscular dystrophies, is clinically characterized by congenital muscular dystrophy (CMD) or later‐onset limb‐girdle muscular dystrophy (LGMD). It may occur with or without brain and ocular abnormalities OMIM 61315 and MDDMembers of a six‐generation consanguineous Han Chinese family with putative muscular dystrophy were enrolled for genetic analysis at the Third Xiangya Hospital, Central South University, Changsha, China. General physical examinations and thorough neurological examinations of all the available family members were performed. Other conditions, including progressive proximal pelvic, shoulder girdle and limb muscle weakness after having acquired ambulation, joint contractures, and moderate or mild MR, revealed a more refined diagnosis http://sift.jcvi.org/) algorithm was used to test possible pathogenic function of non‐synonymous SNPs http://www.ncbi.nlm.nih.gov/projects/SNP/snp_summary.cgi), 1000 genomes project , HapMap project , Exome Variant Server  and in‐house exome BGI database. Only variants, which include non‐synonymous SNPs in exonic regions, coding indels or canonical splice‐site changes, can be regarded as pathogenic candidates Exome sequencing was conducted in the proband of this family  and MutationTaster (http://www.mutationtaster.org/) http://www.fruitfly.org/seq_tools/splice.html) Locus‐specific polymerase chain reaction (PCR) amplification primers were designed for potential pathogenic variants, and Sanger sequencing was performed on the ABI3500 genetic analyzer  via reverse transcription PCR. PCR amplification was conducted using paired primers as follows: 5′‐CACGGGGACATGGTGCAG‐3′ and 5′‐AAGACAGCGGAAGTGTTCAC‐3′, 5′‐CTCTCAGAGGTCCGCTTTGT‐3′ and 5′‐TCGCCATGAAGCTGAGGTT‐3′ Total RNA was isolated from the lymphocytes of family members carrying variants to further determine whether the variants affect messenger RNA (mRNA). Complementary DNA (cDNA) was synthesized POMT1 gene were identified in the proband, which were confirmed in both affected siblings (VI:2 and VI:3). Either no mutation or a heterozygous mutation (c.1338+1G>A or c.1457G>C) was observed in the unaffected family members  and the 100 normal controls after further validation of Sanger sequencing  missense variant would be deleterious. Multiple sequence alignment revealed high conservation of tryptophan at position 486 (p.W486) in the homologous human protein POMT2 and in the POMT1 orthologs (http://blast.ncbi.nlm.nih.gov/Blast.cgi) Exome sequencing in the proband produced about 69.42 million reads with a read length of 90 bp. There were 56.23 million reads aligned to the human genome; 2914.76 Mb were mapped to the target region with a mean coverage of 66.02×. There were 93,994 SNPs, including 11,153 non‐synonymous SNPs in the coding sequence and 2490 in the splice sites, detected. There were 7154 indels, including 412 in the coding sequence and 420 in the splice sites, identified. A prioritized filtration strategy of the variants was carried out following a scheme performed in previous studies POMT1 gene mutations Defective α‐DG glycosylation is the main factor responsible for CMD and LGMD, collectively termed MDDG, which is a genetically heterogeneous group of muscular dystrophies with a wide range of clinical severity POMT1 gene, were detected in members of an intermarriage Han Chinese family with MDDGC1 and cosegregated with the disease status. Generally, genetic disorders in consanguineous families are attributed to homozygous mutations. The compound heterozygous mutations identified in this family indicated that the causative factor was not associated with consanguineous marriage In silico analysis disclosed the damaging function of the p.W486S missense mutation and the high evolutionary conservation of p.W486. These two factors taken together with the confirmed impaired splicing of the c.1338+1G>A mutation demonstrate the possible pathogenic effect of these compound heterozygous POMT1 mutations in the MDDGC1 of this family.In this research, compound heterozygous mutations, including a splice‐site mutation c.1338+1G>A (p.H415Kfs*3) and a missense mutation c.1457G>C (p.W486S) in the POMT1 gene, mapped to chromosome 9q34.13, spans over 20 kb, contains 20 exons and encodes the protein O‐mannosyltransferase 1 (POMT1) with 747 amino acids. The protein and its homologue POMT2 possess protein O‐mannosyltransferase activity and are putatively involved in O‐mannosyl glycan synthesis, important for muscle, brain and eye development The POMT1 gene have been described in the literature at the Human Gene Mutation Database (http://www.hgmd.cf.ac.uk/). The disruptive effect of POMT1 gene mutations can be mediated through the reduced or absent O‐mannosylation of target proteins in a loss‐of‐function mechanism POMT1 gene mutations are identified in MDDGC1 cases, varying from missense and nonsense mutations, through small indel, to gross deletion To date, at least 72 mutations in the POMT1 gene, which is adjacent to the conserved MIR domain, leads to exon 13 being skipped and a reading frame shift and results in a truncated protein. The mutation was previously reported to be responsible for severe type II lissencephaly, classified to MDDGA1, together with a nonsense mutation c.1858C>T (p.R620X) The splice‐site mutation c.1338+1G>A in the POMT1 c.1338+1G>A (p.H415Kfs*3) and c.1457G>C (p.W486S) mutations in compound heterozygotes, responsible for the autosomal recessive MDDGC1 in this pedigree. The exon 13‐skipping effect of a splice‐site mutation was first confirmed by RNA analysis. These findings may enlarge phenotype and mutation spectrum of the POMT1 gene, allowing for greater diagnostic accuracy in exome sequencing and genetic counselling for undiagnosed or ambiguous disorders This is the first known report of The authors confirm that there are no conflicts of interest.


Throughout many countries, professional bodies rely on yearly self-assessment of competence for ongoing registration; therefore, nursing competence is pivotal to safe clinical practice. Our aim was to describe and compare perioperative nurses’ perceptions of competence in four countries, while examining the effect of specialist education and years of experience in the operating room.Perceived Perioperative Competence Scale-Revised (PPCS-R), was used with a total sample of 768 respondents. We used a factorial design to examine the influence of country, years of experience in the operating room and specialist education on nurses’ reported perceived perioperative competence.We conducted a secondary analysis of cross-sectional surveys from four countries including; Australia, Canada, Scotland, and Sweden. The 40-item Regardless of country origin, nurses with specialist qualifications reported higher perceived perioperative competence when compared to nurses without specialist education. However, cross-country differences were dependent on nurses’ number of years of experience in the operating room. Nurses from Sweden with 6–10 years of experience in the operating room reported lower perceived perioperative competence when compared to Australian nurses. In comparing nurses with > 10 years of experience, Swedish nurses reported significantly lower perceived perioperative competence when compared to nurses from Australia, Canada and Scotland.Researchers need to consider educational level and years of experience in the perioperative context when examining constructs such as competence. Throughout many countries, professional bodies rely on yearly self-assessment of competence for ongoing registration . Thus thNurses in countries with distinctly different healthcare systems report similar shortcomings in their work environments associated with the quality of hospital care , 6. PrevWhile previous research has examined differences in nursing practice and healthcare contexts across generalist areas of nursing , 12, theA secondary analysis of cross-sectional surveys from four countries including; Australia, Canada, Scotland, and Sweden was undertaken using the Perceived Perioperative Competence Scale-Revised (PPSC-R) . The aimThe Australian sample was drawn from 575 RNs practising in the OR departments of 3 large metropolitan hospitals in Queensland. The Canadian sample was drawn from 301 RNs working in the OR departments of 3 large inner city hospitals in Toronto, Ontario. The United Kingdom (UK) sample of 203 perioperative nurses and practitioners was drawn from 3 NHS Trusts in eastern Scotland, spanning from Aberdeen to Larbert. The Australian, Canadian and UK samples were drawn using convenience methods. A census sample of 2902 nurses who were members of the Swedish Association of Health Professionals as either OR nurses or Registered Nurse Anaesthetists (RNA) were emailed independently through the Association. Survey data across samples was collected from 2011 to 2016.: Foundational knowledge and skills; Leadership; Collaboration; Proficiency; Empathy; and Professional development [Across the four countries, the PPSC-R  was usedelopment . The totn = 1122). In this sample, the results of the final exploratory factor analysis (EFA) with the 6 factors accounted for 58.3% of the total variance, and Cronbach’s alpha (α) ranged from .81 to .89 [During testing, the PPCS-R demonstrated robust psychometric properties in census sample of Australian perioperative nurses  and its distribution. Respondents’ composite PPCS–R and subscale scores (for the six domains) were measured as continuous variables while gender, primary role, specialty qualifications and years of OR experience were analysed as categorical variables. Cronbach’s alpha (α) was used to determine the internal consistency of the PPCS–R.ηp2) was used as an indication of effect size. Traditionally ηp2 values of 0.01, 0.06 and 0.14 represent small, medium and large effect sizes [p > .05), equal variances were assumed and a Bonferroni correction was used to assess significance for multiple comparisons. When homogeneity was violated p < .05, and equal variances were not assumed and the Games-Howell correction was used to assess significance.Inferential analyses included 4  × 3  × 2  factorial Analysis of Variance (ANOVA). We used an alpha level of 0.05 to determine statistical significance. Partial eta squared , Canada 76.5% (134/175), Scotland 71.1% (214/301), and Sweden 38.5% (1033/2679). Across the four countries, the combined sample of 1557 cases was reduced to a final sample of 768. The Swedish sample had 1033 cases of data and this was disproportionate to the other 3 samples sizes. Thus we used a stratified random sample of 250 cases was selected from the original sample. The 250 cases were stratified by nursing role, resulting in 125 OR nurses and 125 RNAs. However, across the samples 6 cases were also removed due to missing data. The sample demographics for the 4 countries are provided in Table F  = 4.0, p = .047 (ηp2 = .01). A main effect is the effect of one the independent variable i.e., specialist education on the dependent variable PPC-R, ignoring all other independent variables. Overall, respondents who had specialist qualifications reported higher PPC  than those without specialist education .Descriptive results of total PPCS-R scores for each country relative to years of OR experience and specialist qualification are detailed in Table F = 6.5, p < .001 (ηp2 = .03), and years of OR experience F = 58.5, p < .001 (ηp2 = .14). However, these were qualified by a significant interaction between these variables F = 2.5, p = .022 (ηp2 = .02), as shown in Fig. F = 1.23, p = .300. However, country differences did occur in total reported PPC for respondents with 6–10 years of experience F = 4.33, p = .006 (ηp2 = .08) and those with > 10 years of experience in the OR, F = 14.9, p < .001 (ηp2 = .11).There were also significant main effects for Country t = − 3.6(91), p = .001. There were no significant differences between nurses in any other countries. For those with > 10 years of experience, nurses from Sweden reported significantly lower PPC when compared to nurses from Australia t = − 3.7(211), p < .001, Canada t = − 5.8 (162.9), p < .001 and UK t = − 5.2(236), p < .001 respectively. No other significant differences were observed, between nurses with > 10 years’ experience, in reported PPC. Means, standard error and 95% confidence intervals are shown in Table Pairwise comparisons between the countries for respondents with 6–10 years of experience in the OR show that in general, Australian nurses reported higher PPC when compared to the Swedish nurses only, t = − 3.711, p < .less is more, just enough, describes the basis of Swedish national psyche which is characterised by consensus, equity, and using a balanced and moderate approach [In this secondary analysis of cross-national data, we examined the influence of specialist education and years of experience across a four-country sample in reported nurses perceived perioperative competence (PPC). Across the combined sample, 54% of survey respondents had over a decade of perioperative experience. Concomitantly, respondents reporting greater levels of PPC had more years of perioperative experience; thus the effect of years of experience follows the expected trend , 22–24, approach . Thus, wAcross the entire cross-national sample in this study, nearly 60% of perioperative nurses reported having a specialist qualification, with the highest numbers of nurses from Canada (78%) and Scotland 45%). Our findings suggest that there was an effect of specialist education, where nurses regardless of country of origin with specialist qualifications, reported higher levels of PPC when compared to nurses with undergraduate or vocational qualifications. Similar results have been described in a litany of earlier research on the influence of specialist or specialty education relative to self-reported competence , 26, 27.%. Our fiThere may be other reasons, not accounted for in our study to explain cross-country differences noted herein. First, while role may contribute significantly to clinical performance, it is unclear how or why it may affect perceived perioperative competence. In our study, small sample sizes and the differences in role identification across countries precluded analysis based on role categories. Second, the likely cultural differences in the conceptual interpretation of individual words or statements in the scale may have indirectly influenced our results. Finally, organisational macro-management strategies based on professional rewards may have a positive impact on nurse competence. For instance, competency-based management approaches that accurately assess a nurse’s performance, skills and abilities have the potential to increase nurses’ understanding of their strengths and weaknesses . Such apAlthough the PPC-R has been psychometrically evaluated in English  and SwedTo our knowledge, this is the first study to describe cross-country comparisons relative to perioperative nurses’ perceived competence based on country, years of experience and specialty education. Our results also suggest that researchers need to take into account education qualifications and years of experience in the perioperative context when examining constructs such as competence. Cross-country comparative research contributes significantly to the body of knowledge about a specific country as well as providing information on important factors researchers need to consider when evaluating eclectic constructs such as competence in nursing samples. As such, our results may contribute to the examination of population norms for researchers who may want to evaluate perceived competence.


Guided by the Socialization Model of Child Behavior (SMCB), this cross-sectional study examined direct and indirect associations of parental cognitions and behavior, the home and neighborhood environment, and toddlers’ personal attributes with toddlers’ physical activity and screen time.Participants included 193 toddlers (1.6 ± 0.2 years) from the Parents’ Role in Establishing healthy Physical activity and Sedentary behavior habits (PREPS) project. Toddlers’ screen time and personal attributes, physical activity- or screen time-specific parental cognitions and behaviors, and the home and neighborhood environment were measured via parental-report using the PREPS questionnaire. Accelerometry-measured physical activity was available in 123 toddlers. Bayesian estimation in structural equation modeling (SEM) using the Markov Chain Monte Carlo algorithm was performed to test an SMCB hypothesized model. Covariates included toddlers’ age, sex, race/ethnicity, main type of childcare, and family household income.β = 0.451), while higher parental negative outcome expectations for limiting toddlers’ screen time was associated with lower parental screen time limiting practices (β = − 0.147). In turn, higher parental screen time limiting practices was associated with lower screen time among toddlers (β = − 0.179). Parental modeling of higher screen time was associated with higher screen time among toddlers directly (β = 0.212) and indirectly through the home environment. Specifically, higher screen time among parents was associated with having at least one electronic device in toddlers’ bedrooms (β = 0.146) and, in turn, having electronics in the bedroom, compared to none, was associated with higher screen time among toddlers (β = 0.250). Neighborhood safety was not associated with toddlers’ screen time in the SEM analysis. No significant correlations were observed between the SMCB variables and toddlers’ physical activity; thus, no further analyses were performed for physical activity.In the SMCB hypothesized screen time model, higher parental barrier self-efficacy for limiting toddlers’ screen time was associated with higher parental screen time limiting practices (Parents and their interactions with the home environment may play an important role in shaping toddlers’ screen time. Findings can inform family-based interventions aiming to minimize toddlers’ screen time. Future research is needed to identify correlates of toddlers’ physical activity. Canadian 24-Hour Movement Guidelines for the Early Years ) were truncated below ±3 SD. In addition, 22 parents who exceeded 180 min/day of walking, MPA, or VPA variables were truncated to be equal to 180 min/day according to the IPAQ scoring protocol (ess (±2) –51, thusthan 0.5 .To operationalize the SMCB  for struSince a binary outcome was part of our hypothesized model , the Markov Chain Monte Carlo algorithm (MCMC) methods were employed to obtain Bayesian estimation in SEM. Unlike frequentist analysis , Bayesian analysis provides parameter estimates from background knowledge  to inform new data , and MCMC sampling methods achieve these estimates from the mean of the posterior distribution , 58. Thea ≤ θ ≤ b) = 0.95) indicates that one is 95% sure that the true value of θ lies between a and b [p values. The model was determined as well-fitted when posterior predictive p value was close to 0.50 [In this study, the relationships between variables are largely unknown, therefore we used uninformative (diffuse) prior. This method is thought to have minimum impact on the model estimates . The hyp a and b . Stable  a and b . Model f to 0.50 . In Baye to 0.50 . In this to 0.50 . Specifin = 193) and excluded (n = ranged from 44 to 64 due to missing cases), no significant differences were observed in toddlers’ age (1.6 ± 0.2 vs. 1.6 ± 0.4), sex (boys: 49.2% vs. 47.8%), race/ethnicity (Caucasian/European-Canadian descent: 53.9% vs. 55.6%), household income , or the main type of childcare .A total of 203 out of the 257 participants had complete data on the key SMCB variables from the PREPS questionnaire. As for demographic variables, 12 participants did not respond or responded “do not know” to the household income question and one participant did not indicate race/ethnicity, leaving a total sample of 193 toddlers for the analyses involving screen time. Between the samples of included (n = 123) and excluded (n = 70) participants, no significant differences were observed in toddlers’ age (1.6 ± 0.2 vs. 1.6 ± 0.3), sex (boys: 53.7% vs. 45.7%), race/ethnicity (Caucasian/European-Canadian descent: 58.5% vs. 45.7%), household income , or the main type of childcare . A detailed description of participant characteristics is presented in Table A total of 123 out of the 193 participants provided complete accelerometry data and were included in the analyses involving physical activity. Between samples of included (r = − 0.407), barrier self-efficacy for limiting screen time (r = − 0.432), and neighborhood safety (r = − 0.310). Variables that were significantly positively correlated with toddlers’ screen time included parental modeling of screen time (r = 0.302), negative outcome expectations for limiting screen time (r = 0.414), and having at least one electronic device in the bedroom (r = 0.312). The strength of these correlations was moderate. Three variables not significantly correlated with toddlers’ screen time  were removed from the hypothesized model and excluded from further analysis. No significant correlations were observed between the SMCB physical activity variables and MVPA or TPA   see .β = − 0.147, 95% CI = − 0.169, − 0.125), and in turn, lower parental limits on toddlers’ screen time was associated with higher screen time among toddlers  Similarly, higher barrier self-efficacy for limiting toddlers’ screen time was associated with higher parental limits on toddlers’ screen time , and in turn, higher parental limits on toddlers’ screen time was associated with lower screen time among toddlers .The SMCB hypothesized screen time model is shown in Fig. β = 0.212, 95% CI = 0.099, 0.325). In addition, higher parental modeling of screen time was associated with having at least one electronic device in the bedroom  and, in turn, having at least one electronic device in the bedroom, compared to having no electronic device in the bedroom, was associated with higher screen time among toddlers . No plausible direct and indirect associations were observed between neighborhood safety and toddlers’ screen time.Parental modeling did not mediate the relationship between parental cognitions and toddlers’ screen time. However, higher parental modeling of screen time was directly associated with higher screen time among toddlers  and behaviors , and the home environment (presence of electronic devices in the bedroom) were independently and simultaneously associated with toddlers’ screen time. Specifically, parental cognitions were indirectly associated with toddlers’ screen time through parental screen time limits. Parental modeling of screen time was directly associated with toddlers’ screen time and indirectly through having at least one device in the bedroom. However, neighborhood safety, which was significantly correlated with toddlers’ screen time was not associated with toddlers’ screen time in the SEM analysis. Additionally, no correlates of physical activity, in terms of TPA, LPA, or MVPA were identified.The finding in the present study that parental cognitive factors were significantly correlated with toddlers’ screen time is consistent with two previous studies , 62. SpeParental cognitive factors were not associated with parental modeling of screen time; however, parental modeling of screen time was directly associated with toddlers’ screen time. Combined, the findings from this study add to the growing evidence that parental behavior, including limits and modeling, is important in shaping screen time behavior during early childhood , 22. ThoThe roles electronic devices play in young families’ lives are an important area of future inquiry to inform the creation of healthy home environments. Findings suggest the location of the electronic devices  is a more important correlate of toddlers’ screen time than the total number of devices in the home. Interestingly, having an electronic device in the bedroom is a consistent correlate of screen time across early years and school-aged groups –73. TherIn contrast to the home environment, the neighborhood environment was not associated with toddlers’ screen time in the SEM analysis. Unlike the home environment, parents do not have direct control over the environment of their neighborhood apart from the potential selection of what neighborhood to live in. Thus, neighborhood safety was included as an exogenous variable in which its value is not dependent on other variables in the model. More empirical evidence exists with older children and adolescents that neighborhood safety is associated with behaviors including screen time , 75–77. This study was the first to demonstrate the dynamic and complex roles of parental cognitions, parental behaviors, and the home environment in determining toddlers’ screen time patterns. Findings can help to inform targets for future interventions aiming to establish healthy screen time behaviors among toddlers. For example, identifying types of support that parents need in limiting toddler’s screen time, and subsequently providing appropriate, feasible, and sustainable resources and strategies is warranted. Also, providing opportunities to cultivate confidence in encouraging alternative behaviors  may increase parental barrier self-efficacy and decrease negative outcome expectations for limiting toddlers’ screen time. Given that maternal self-efficacy for limiting TV viewing has shown to track over time , targetiIn contrast to toddlers’ screen time, findings of the present study with regard to toddlers’ physical activity were not supported by the SMCB model. It is possible that other important correlates of toddlers’ physical activity may have been missed. Previous studies examining individual correlates of physical activity in the toddler age group have observed some significant associations with correlates not examined in the present study. For example, more maternal-child interactions  and loweThe major strengths of this study are the relatively large sample of toddlers and the objective measure of physical activity. The use of structural equation modeling with the application of Bayes’ theorem, which allowed us to simultaneously evaluate the associations among multiple important variables assessed via the hypothesized model is another strength. Furthermore, these methods are known to improve estimation accuracy in complex statistical models such as structural equation modeling under cases of small sample sizes. In addition, Bayesian methods using MCMC allow structural equation models to have binary outcome variables. Other study strengths include the theoretical underpinning and the demographically and socioeconomically diverse sample. Lastly, this study included behavior-specific correlates for physical activity and screen time within the same sample.A limitation of this study is the use of proxy-reported data except for toddlers’ physical activity; thus, our findings are subject to information bias . Nonetheless, psychometric properties of the PREPS questionnaire are previously established . The croParents appear to play a critical role in establishing healthy screen time behaviors among their toddlers by practicing screen time limits, role modeling healthy screen time behaviors themselves, and creating screen-free bedrooms within the home environment. However, important correlates of physical activity in toddlers are currently unclear. Further investigation is required to build on our findings with regard to the potential role of parental and environmental characteristics in toddlers’ screen time using longitudinal and experimental study designs to confirm causality. Given that toddlerhood is a window of opportunity for establishing healthy behavioral patterns at home, and that the design of our hypothesized model is guided by the SMCB model, our results can help inform future family-based interventions targeting screen time among toddlers.