However, quantitative MRI values are altered by tissue fixation. Therefore, the purpose of this study was to investigate time-dependent effects of formalin fixation on MRI relaxometry (T1) and transverse (T2) relaxation times, FA, MD, and MWF were generated at each time point to illustrate spatiotemporal changes, and region-of-interest analyses were then performed in eight brain structures to quantify temporal changes with progressive fixation.Two whole, neurologically-healthy human brains were immersed in 10% formalin solution and scanned at 13 time points between 0 and 1,032 h. Whole-brain maps of longitudinal  our final measurements were taken at 1,032 h.Although neither of the diffusion measures (FA nor MD) showed significant changes as a function of formalin fixation time, both T1-relaxation, T2-relaxation and MWF estimates must be performed quite early in the fixation process to avoid formalin-induced changes compared to in vivo values; and furthermore, that different ex vivo scans within an experiment must be acquired at consistent  fixation intervals to avoid fixative-related differences between samples. Conversely, ex vivo diffusion measures (FA and MD) appear to depend more on other factors .These results suggest that T From io (SNR) –8. As a ng (DTI) , relaxomand T2*) –13, and ng (MWI) . For exaclerosis –18. Thus1- and T2-relaxation times of tissues compared to their in vivo state after a certain period , and T2-relaxation based MWF measurements in a number of deep gray matter (GM) and white matter (WM) structures to explain the possible implications of progressive formalin fixation in whole human brain specimens. Although fixation-induced relaxation and diffusion changes have been previously investigated in human brain tissue, the current study is (to the best of our knowledge) the first characterization of MWF changes in whole formalin-fixed brains. Given that fixation has been shown to shorten T2-relaxation times, and MWF estimates are derived from short T2-relaxation water compartments, we hypothesize that MWF measurements will be artificially inflated as a function of tissue fixation. However, no previous studies have characterized this phenomenon. Therefore, by empirically measuring all of these signals over time , our findings are expected to have important implications for future postmortem brain MRI studies by elucidating the time-course of ex vivo MRI signal changes in whole, fixed human brain samples.In order to better address some of the concerns related to postmortem human brain imaging, it is important to characterize the changes of different MRI properties in non-diseased human brains due to formalin fixation. Thus, in this work, we examine the quantitative changes of DTI-based fractional anisotropy (FA) and mean diffusivity (MD), relaxation times . Two human brain specimens—both female —were obtained with prior consent. Each patient’s medical history was pre-screened by a neuropathologist to exclude cases with neurological disorders or medico-legal cases . Both brains were subjected to a standard neuropathology examination following the study to ensure that incidental pathological findings were not identified.ex vivo brain MRI protocol . Each brain, including the dura mater, was then carefully removed as soon as possible , a MRI fiducial marker was attached to confirm the orientation of cerebral hemispheres in subsequent images, and the whole brain was immersed in 10% phosphate buffered formalin solution (Sigma-Aldrich Product ID: HT501128) inside a 3.8-L air- and liquid-tight MRI-compatible container using a previously reported protocol . For Sub–2 h. EacEach brain specimen was scanned at room temperature (22 ± 1°C) at the same 13 time points  after the initiation of formalin fixation. All MRI scans were performed as previously described , in a whb = 700 s/mm2; maximum gradient amplitude = 45 mT/m; gradient duration (delta) = 20.2 ms; effective diffusion time (DELTA) = 25.7 ms], 5 reference images (b = 0 s/mm2), repetition time (TR) = 8,800 ms, echo time (TE) = 73.6 ms, flip angles = 90° (excitation) and 180° (refocusing), receiver bandwidth = 3,064 Hz/pixel, fat suppression = on, number of slices = 55, slice thickness = 2.5 mm, matrix size = 192 × 192, FOV = 240 mm × 240 mm, resolution = 1.25 mm × 1.25 mm × 2.5 mm, number of averages = 2, total scan time = 11 min. T1-relaxometry data were acquired using a 3D magnetization prepared rapid acquisition gradient echo (MPRAGE) sequence with two inversion times and two flip angles (MP2RAGE)  of Subject 1. As a result, conventional T1-weighted images were obtained using a conventional 3D MPRAGE sequence . The parameters for the conventional MPRAGE sequence were TR = 1,900 ms, TE = 2.49 ms, TI = 900 ms, flip angle = 9°, receiver bandwidth = 170 Hz/pixel, fat suppression = off, matrix size = 512 × 512 × 176, resolution = 0.49 mm × 0.49 mm × 0.98 mm, FOV = 250 mm × 250 mm × 172 mm, and scan time = 5 min. A multi-echo 3D combined gradient and spin echo (GRASE) sequence was used to acquire images with 32 different echo times  .1 The pasequence  in orderho times  and was urements . Scan paBaseline  SNR calculations were then performed on mid-axial 2D images obtained from each pulse sequence. This was achieved by dividing the average signal intensity within the brain by the SD of the noise in air outside of the bucket. The resulting SNR values for each pulse sequence are shown for Subject 2 in Table , (2) Medical Image Processing, Analysis, and Visualization (MIPAV) , (3) MATLAB , (4) Statistical Parametric Mapping (SPM12) , and (5) MRIStudio . Please note that detailed, step-by-step instructions describing how the images were processed are available in the Supplementary Material.All images were preprocessed with a customized pipeline, using a combination of software tools, including: (1) The FMRIB software library 1-weighted images. These binary masks were dilated in MIPAV using a 5-mm3 kernel before they were used for skull-stripping. All axial DTI images were eddy current corrected and motion corrected before generating FA and MD maps using the Fit Diffusion Tensor tool  refocusing pulses] (2 fitting for different water compartments while compensating for stimulated and indirect echoes due to imperfect refocusing pulses (2 distribution from myelin water (~10–40 ms) divided by the geometric mean of the T2 distribution due to intra- and extra-cellular water (~100–200 ms) plus the myelin water (~10–40 ms), as previously described  space  from each time point. Since the T1-weighted image contrast was reduced and the shape of the brain was presumed to be constant (due to the fixation beyond 120 h) during time points 5–13, the normalization parameters from time point 4 were applied to subsequent time points after co-registering the native-space images in SPM12.In order to spatially normalize the data from both subjects, all time points, and all contrasts, the TI) space . This waI) space  with casI) space . Later, 1-relaxometry, T2-relaxometry, and MWF maps from each of the subsequent time points. Region-of-interest (ROI) analyses were performed using ROIEditor and 3D ROIs chosen from the JHU_MNI_SS (“Eve”) atlas in four deep WM and four subcortical GM structures, including genu of the corpus callosum (GCC), splenium of the corpus callosum (SCC), optic radiation (OR), internal capsule (IC), putamen (Put), thalamus (TH), globus pallidus (GP), and caudate nucleus (CN). Figure 1, T2, and MWF values for each subject were averaged across hemispheres.After all of the images were normalized, SPM12 was then used to map the spatiotemporal changes due to formalin fixation—on a voxel-wise level throughout the entire brain—by subtracting the baseline FA, MD, T. Spearman rank-correlation coefficient (sr) was used to assess correlations between MRI measures and fixation time. Two-tailed tests were performed, and only ROIs showing Bonferroni corrected p-values less than 0.05  were considered to be statistically significant.All data were analyzed using SPSS 23.0 In order to examine tissue autolysis and degradation, and in particular, to confirm that myelin was not visibly compromised/degenerated throughout the time-course of each experiment, deep WM tissue sections were obtained from each brain specimen following the last MRI scan . Specifically, tissue sections with a thickness of 5 µm were obtained from deep WM regions, stained with Eriochrome Cyanine R , and photographed at 400× magnification using an Olympus BX53 microscope with CellSens imaging software . With this preparation, myelin is selectively stained blue , 46,2 thIt is worth noting that Eriochrome Cyanine-stained tissue sections obtained from each brain after the final, 1,032 h MRI scans appear to show: (1) little, if any, evidence of generalized autolysis and (2) large numbers of myelinated axons within the deep WM  are shown in Figure 1, T2, and MWF) over fixation time (in hours) for eight deep WM and GM structures, as shown in Figures 1, T2, and MWF maps). Corresponding correlation coefficients and p-values are presented in Table The percent change maps for FA, MD, Tp > 0.05 in six out of eight structures). In both subjects, OR  showed gradual, albeit statistically significant decreases, and Subject 1 and Subject 2 showed statistically significant increases in the SCC (p = 3.8 × 10−4) and TH (p = 0.001) respectively. Similarly, no significant changes in MD values , while Subject 2 showed strong negative correlations in the TH (p = 6.1 × 10−4). However, the baseline  FA values in WM regions were generally higher than those in GM regions, and the baseline MD values in WM regions were generally lower than those in GM regions, as expected  less affected by formalin fixation than either relaxometry or myelin water measurements. No systematic differences were observed in the whole-brain difference (percent change) maps Figure , and theexpected .1 and T2) were significantly affected by formalin fixation. Clear spatiotemporal changes were observed across the entire brain  these reductions were statistically significant in both WM and GM structures   or CN (p = 0.55) for Subject 2.For both subjects, the relaxation times  Figure . Similars Figure . Howeverp < 0.05) in all eight WM and GM regions for Subject 1, and seven out of eight regions for Subject 2 , it should be noted that all of the MWF estimates beyond the third time point  were still at least twice as large as the initial baseline value.Finally, MWF measurements showed clear fixation-related changes across the entire brain Figure , and the2 Figure . However1-relaxation, T2-relaxation, and MWF measurements  throughout whole human brain samples and have characterized these changes in several WM and GM tissue structures. We found that formalin fixation did not systematically effect diffusion measurements  as significantly as other quantitative measures: namely, T1-relaxation times, T2-relaxation times and MWF estimates, which were progressively altered by tissue fixation.We have demonstrated the time-dependent effects of formalin fixation on various quantitative MRI measurements  and body temperature (~37°C) means that the actual amount of water diffusion is reduced compared to in vivo conditions , which is why we sought to use stronger diffusion-encoding than some earlier ex vivo diffusion imaging studies (p < 0.001) in both specimens, but (2) changes in MD values over time were either not significant  or less significant .Although the diffusion-weighted FA and MD values in the current study were not significantly correlated with formalin fixation time, we cannot conclude from these measurements that fixation did not affect diffusion. Rather, the fact that large signal fluctuations were observed between consecutive scans Figures  and 6 su studies . It is w studies . These r2-relaxation times are decreased with progressive fixation , as in the current experiment—inherently confounds the effects of diffusion and T2-relaxation changes. Nonetheless, despite these confounds and the fact that higher amounts of diffusion-sensitivity also comes at the expense of SNR  that were not possible in the current experiment.r1 = −0.8, p1 = 9.6 × 10−4; r2 = −0.78, p2 = 0.001) was observed in the OR for both subjects. This fiber bundle has higher myelin density than the surrounding tissue  for these MRI changes. Although it was not possible to investigate in our current study, future experiments may be able to test this hypothesis by, for example, taking measurements from a formalin-fixed brain and then washing out the fixative and rehydrating the brain tissue in order to determine whether MWF values are decreased back to in vivo (or early ex vivo) levels. Nonetheless, our findings clearly indicate that even small differences in fixation time between tissue samples could have large effects on the measurements, and that researchers must exercise extreme caution when comparing in vivo MWF values with ex vivo values .In stark contrast to relaxometry measures, the MWF values increased rapidly and all regions showed highly significant positive correlations with fixation time—with the exception of the GCC and CN for Subject 2. To the best of our knowledge, this is the first study to investigate relationships between MWF measurements and tissue fixation; and, although our data do not speak directly to the exact mechanisms underlying these MWF changes, we predict that there are two simultaneous effects that may be contributing. First, there is likely an increase in the volume of “myelin water”  due to oxygen and glucose deprivation, which have previously been linked to changes in myelin layer densities in brain tissue . If the ar water  in brainex vivo diffusion measurements).Despite our best efforts to optimize the experimental design, acquisition, and analysis, there are a few noteworthy limitations in this study  that tissues were not structurally compromised throughout the relatively long experimental time-course. Under normal circumstances, sectioning and staining should be performed much sooner  in order to avoid protein cross-linking and other potential fixation-related immunohistochemistry artifacts.n = 2) and PMIs were not as short or consistent as desired (in order to inspect the immediate postmortem changes and minimize possible tissue degradation prior to fixation). Therefore, although both specimens were refrigerated almost immediately after patient death, differences in PMI  and any associated tissue degradation prior to fixation may have had an effect on the results, as PMI is known to affect MRI properties  our sample size was small  that are violated as T1-relaxation times stray farther from the optimized range of values. As a result, the accuracy of our MP2RAGE-based T1 measurements may have been decreased at later fixation times . However, the changes observed between the first and last measurements, although statistically significant, were still on the same order of magnitude ; and perhaps even more importantly, we reported and based our conclusions on Spearman rank correlations (as opposed to linear Pearson correlations), which do not depend on the absolute values of the measurements, but rather the overall trends  over time.Another limitation was that, in order to reduce the overall scan time for each session (given that there were 26 sessions), T2-relaxometry was performed using multi-echo 3D GRASE images from the MWI sequence with a relatively short TR: thereby introducing more T1-weighting than traditional T2-relaxometry methods. However, we did not have enough time in each session to acquire separate spin echo based T2-relaxometry data, and since the amount of T1-weighting was constant across samples and time points, these effects are presumed to be minimal. Nonetheless, since significant T1-relaxation changes were identified, there may have been some interaction between T1 and T2 effects in the T2-relaxation time measurements.TAt least one previous study noted that the position of the tissue in formalin should be taken into consideration while characterizing longitudinal effects of fixation ; however1-relaxation, T2-relaxation, and MWF values in whole postmortem human brains. The T1, T2, and MWF changes indicate that formalin gradually diffuses inward from the cortical surface, and tissue fixation continues to affect MR properties until (and likely beyond) our maximum fixation time of 1,032 h. Given the significant and almost immediate effects of formalin fixation on ex vivo relaxometry (T1 and T2) mapping and MWI measurements, our results indicate that future ex vivo relaxometry and MWI studies should consider scanning: (1) in situ immediately following death (if feasible), or (2) ex vivo as soon as possible following both death  and immersion in formalin —preferably within the first 120 h of fixation (if possible). Our results suggest that even scanning different brains at a consistent post-fixation interval—especially between 120 and 1,032 h following initiation of formalin fixation—may lead to significantly different absolute measurements between samples, which could in turn lead to erroneous conclusions. On the other hand, ex vivo diffusion imaging measures (FA and MD) appeared to be more affected by factors  other than formalin, even after prolonged fixation.In this study, we characterized the time-dependent effects of formalin fixation on FA, MD, Tex vivo brain imaging at early time points could provide quantitative MRI data that may be useful in its own right, and also potentially guide subsequent brain sectioning and immunohistochemistry studies, which are typically performed at later postmortem intervals .Finally, considering the relatively low cost of the fixation and imaging protocols employed in the current study, these practices should be highly adoptable not only for future research applications but also in forensic autopsy practice, since it provides high quality images with a number of different tissue contrasts within a clinically acceptable time-frame. In this regard, This study was conducted with prior approval from the University of Manitoba Biomedical Research Ethics Board (BREB).AS, KM, and CF acquired the data; AS, MU, KM, and CF were involved in data analysis and interpretation, and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Although previous evidence has shown that physical activity and physical fitness lower the level of psychological distress, little is known of simultaneous associations between of physical activity and physical fitness and with psychological distress, especially in young adults. Therefore, the main purpose of the present study was to explore both separate and simultaneous association between physical activity and physical fitness with psychological distress.Participants in this cross-sectional study were 2,100 university students  chosen from eight faculties in the city of Zagreb. Physical activity, physical fitness and psychological distress were assessed using structured questionnaires. The associations were examined using logistic regression analysis.After adjusting for gender, body-mass index, self-rated health, material status, binge drinking, chronic disease/s and sleep quality, “insufficient” physical activity  and “lower” levels of physical fitness  remained associated with “high” psychological distress. When physical activity and physical fitness were entered simultaneously into the model, “insufficient” physical activity  and “lower” levels of physical fitness  remained associated with “high” psychological distress.Our study shows that both “insufficient” physical activity and “lower” levels of physical fitness are associated with “high” psychological distress, even after adjusting for numerous covariates. Therefore, special policies aiming to increase the levels of physical activity and fitness are warranted. Mental health disorders have become one of the main public health problems worldwide, with special increasing prevalence among youth . ApproxiTreatment for depression is antidepressant medication , which iPrevious studies have tried to explain the complexity and mutual processes of biological and psychological factors that physical activity has on mental health . In termThus, according to the literature, it is well-established, that both physical activity and physical fitness have beneficial role on mental health. However, little is known about the relationship these two potential factors with mental health in young adults. Young adulthood is characterized by the onset of which mental health problems start to occur . On the Since young adults represent a risk group for higher levels of mental health disorders and lower levels of physical activity, it is necessary to explore these associations. Therefore, the main purpose of the present study was to explore both separate and simultaneous associations of physical activity and physical fitness with psychological distress in a large sample of young adults.p = 0.21–0.74). All the analysis and procedures were anonymous and in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Faculty of Kinesiology (Ethics code: 16/2017). Also, all participants gave their written informed consent for participation in the study.This study was conducted among university students in Zagreb, the capital city of Croatia with approximately 1,000,000 citizens. The University of Zagreb is composed of 33 faculties (departments) and between 65,000 and 70,000 students attend the University every year. A random sampling approach was used to select faculties. At the first stage, we randomly selected eight out of 33 faculties. The randomization was done with replacement, where each faculty had unique number and was drawn from the box. At the second stage, we contacted teachers from each faculty to help us organize the sampling procedure. A recruitment announcement was sent via emails and e-newsletter to the teachers with a request to pass the study information to students. All eight faculties agreed to take part in the study, representing 2,320 students enrolled in the 2017 academic year. Of these, 2,100 students  provided full data (90.5%) and were enrolled in further analysis. Students came from a variety of social , technical  and health-related  sciences. Before the main analysis, we examined the differences between the participants and non-participants in terms of gender, age, body-mass index, self-rated health and psychological distress. No significant differences were observed and no potential bias was made  “How often during the past 30 days did you feel nervous?,” (2) “How often during the past 30 days did you feel hopeless?,” (3) “How often during the past 30 days did you feel restless or fidgety?,” (4) “How often during the past 30 days did you feel so depressed that nothing could cheer you up?,” (5) “How often during the past 30 days did you feel that everything was an effort?” and (6) “How often during the past 30 days did you feel worthless?” . Each quTo assess physical activity in the last seven days, we used International Physical Activity questionnaire, a reliable and valid instrument designed to measure physical activity in respondents between ages 18 and 65 . SpecifiSelf-rated physical fitness was assessed by using one-time question: “How would you rate your physical fitness?” ranging from one (very poor) to 10 (excellent) . This me2) was calculated. Before the study began, we had chosen 35 men and 40 women to validate self-reported height and weight with the objective measure taken by trained survey staff. Pearson’s coefficient of correlation showed excellent relationship between two measures in men (r = 0.96) and women (r = 0.97). For the purpose of this study, we divided body-mass index score into two categories: (1) normal (<25 kg/m2) vs. (2) overweight/obesity (≥25 kg/m2). Although not appropriate as a clinical tool, self-reported BMI serves as a valid tool for epidemiological surveys, especially in young adults  and percentages (%). Differences between categorical variables between “low” and “high” psychological distress were analyzed using Chi-square test. To explore the associations between physical activity and physical fitness with psychological distress, we performed a set of logistic regression analyses. We calculated odd ratios (ORs) with 95% confidence intervals (95% CIs). “High” psychological distress was the main outcome of the present study. First, we explored the association between “insufficient” physical activity and “high” psychological distress in model 1. Second, we explored the association between “low” physical fitness and “high” psychological distress. Since physical fitness was assessed by a 10-item scale, we calculated median and interquartile range (25th and 75th percentile range), in order to categorize participants into three groups (tertiles): (1) <25th percentile as “low,” (2) 25th–75th percentiles as “medium” and (3) >75th percentile as “high” physical fitness. Finally, we entered both physical activity and physical fitness simultaneously into the model (model 3). Significance was set up at α ≤ 0.05 and it was one-sided. All the analysis were performed in Statistical Package for Social Sciences Software, ver. 22 .Basic descriptive statistics of the study participants are presented as frequencies  and variance inflation factors test showed no multicollinearity (1.00–1.10).The associations between physical activity, physical fitness and psychological distress are presented in The main purpose of the present study was to explore both separate and simultaneous associations between physical activity and physical fitness and psychological distress in a large sample of young adults. Both “insufficient” physical activity and “lower” levels of physical fitness were associated with “high” psychological distress after adjusting for gender, body-mass index, self-rated health, material status, binge drinking, chronic disease/s and sleep quality.Our results are in line with previous studies aiming to explore the associations between physical activity/fitness and mental health . SpecifiThe mechanism underlying the association between physical activity/fitness and mental health is not clearly understood . PreviouOur study has some limitations. First, we used a cross-sectional design, in order to determine the associations between physical activity and physical fitness with psychological distress. To determine the causality, Our results show strong associations between “insufficient” physical activity and “lower” levels of physical fitness with “high” psychological distress in a large sample of young adults. Findings of this study should be taken into account when establishing and implementing special strategies and policies that leverage higher participation in physical activity in order to decrease “high” psychological distress.10.7717/peerj.4700/supp-1Supplemental Information 1Click here for additional data file.


Using a large longitudinal data set spanning 4 years, we examined whether a change in self-reported sleep duration is associated with metabolic syndrome (MetS). Current analysis included 15,753 participants who were free of MetS during both 2006–2007 and 2010–2011. Sleep duration was categorized into seven groups: ≤5.5 h, 6.0–6.5 h, 7.0 h, 7.5–8.0 h, ≥8.5 h, decrease ≥2 h, and increase ≥2 h. Cox proportional hazards models were used to calculate hazard ratios (HRs) and their confidence intervals (CI) for MetS, according to sleep duration. Compared to the reference group of persistent 7-h sleepers, a decrease of ≥2 h sleep per night was associated with a higher risk of incident MetS  in analyses adjusted for age, sex, sleep duration at baseline, marital status, monthly income per family member, education level, smoking status, drinking status, physical activity, body mass index, snoring status and resting heart rate. An increased risk of MetS incidence was also observed in persistent short sleepers . This study suggests individuals whose sleep duration decreases ≥2 h per night are at an increased risk of MetS. Metabolic syndrome (MetS) is closely linked to lifestyle and clustering of risk factors, including abdominal adiposity, hypertension, hypertriglyceridemia, low high-density lipoprotein (HDL) cholesterol and hyperglycemia. Given that MetS is becoming a pandemic, with prevalence rates between 20 and 30% among the adult population234679Increasing concerns about reductions in night time sleep duration caused by our new 24/7 society1167In this study, we examined potential longitudinal associations between changes in sleep duration over a 4-year exposure period  developed MetS. Age, sex, sleep duration at baseline, sex, marital status, monthly income per family member, education level, smoking status, drinking status, physical activity, body mass index, snoring status and resting heart rate were designated as confounding factors in Model 2. After adjusting for these confounding factors, participants who slept ≤5.5 h per night had an increased risk of developing MetS compared with participants who persistently slept 7 h . Compared with the reference group of persistent 7-h sleepers, a decrease of ≥2 h sleep per night was associated with a higher risk of MetS incidence  . MoreoveOur findings suggest persistent short sleep (average <5.5 h) increases the risk of MetS incidence. In contrast, increased sleep duration was not associated with higher incidence of MetS compared with individuals persistently sleeping 7 h per night. Compared to these individuals, we also found that a decrease of ≥2 h in sleep duration over a four-year exposure period was associated with an increased risk of developing MetS in analyses adjusted for age, sex, sleep duration at baseline, sex, marital status, monthly income per family member, education level, smoking status, drinking status, physical activity, body mass index, snoring status and resting heart rate. A set of sensitivity analyses further confirmed these findings.After 3.4 years of follow-up, the overall incidence of MetS in this study was 40.0%, which was significantly higher than a previously published report about midlife adults comprising a sub-cohort of ARIRANG and Quebec Family Study patients6726791913222324An inherent limitation of previous studies has been the reliance on a single time point by which to assess sleep duration, which may have occurred several decades prior to the event, and is therefore likely to yield biased estimates of the association. Moreover, there has been no consideration of how sleep duration varies within individuals over time, and the subsequent impact this could have on changes in sleep duration and future risk of MetS. As the current study appears to be the first to demonstrate an association between decreased sleep duration and incidence of MetS in a large cohort study from China, our findings thus provide the first evidence that change in sleep duration is not only strongly associated with future risk of MetS, it is also likely to be a more accurate indicator of the true magnitude of risk as compared with a single measure of sleep duration self-reported several years before the onset of MetS.It is an interesting and new observation that a decrease in sleep duration over a 4-year period is more deleterious than persistent short sleep. This may be related to the shortened sleep duration (≤5.5 h) among sleepers with a significant decrease in sleep duration. There are a number of biological mechanisms through which reduced sleep duration may lead to MetS. Experiments have demonstrated short-term sleep deprivation among healthy subjects results in adverse physiological changes, including decreased glucose tolerance and increased insulin resistance, sympathetic tone and blood pressureThe strengths of our study include a prospective cohort design, large sample size, the Asian ethnicity of our participants, and a broad spectrum of potential confounding parameters. However, there are several potential limitations of our study. First, we only collected information on sleep duration by self-reported questionnaires, without 24-h polysomnography information. However, self-reported measures showed good agreement when compared to quantitative sleep assessment with monitoring30In conclusion, our findings demonstrate an association between reduced sleep duration and increased future risk of MetS. This study also highlights the need to take into consideration change of sleep duration when estimating risk, rather than relying on a single measure of exposure that often precedes the outcome by several decades. Our results should encourage and support individuals to maintain or adopt a 7-h sleep duration each night, as this could have significant beneficial effects in stemming the growing prevalence of MetS.In compliance with the Declaration of Helsinki, the protocol for this study was approved by the Ethics Committee of Kailuan General Hospital and all participants provided informed written consent with signatures34The Kailuan study was a prospective cohort study designed to investigate the association of risk factors and chronic disease. The Kailuan community, located at the center of the Kailuan Coal Industry in Hebei Province, China, has approximately 7.2 million inhabitants with 11 hospitals responsible for their healthcare. From June 2006 to October 2007, a total of 155,418 employees  in the community were invited to participate and 65.31% of them agreed to be participants. A total of 101,510 participants  were recruited into the Kailuan study. During baseline analysis, a total of 85,757 participants were excluded from the recruited population, including 33,651 participants lacking face-to-face follow-up data during 2010–2011 survey, 43,154 participants diagnosed with MetS prior to the 2010–2011 survey, and 6,176 participants lacking complete data regarding sleep duration and other indicators. In addition, 2,776 individuals who did not participate in the 2012–2013 and 2014–2015 surveys were excluded. The remaining 15,753 participants were included in the final analysis . Follow-All participants underwent a clinical examination and standardized interview. Physical activity was evaluated based on individual responses to questions regarding the types and frequencies of physical activity at work and during leisure time. Physical activity was classified as “≥4 times per week and ≥20 min at a time”, “<80 min per week”, or “none”. Smoking and drinking statuses were classified as “never”, “former”, or “current” according to self-reported information. Monthly income per family member (at baseline) was categorized as “<¥600”, “¥600–799”, “¥800–999” and “≥¥1,000”.2). Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured thrice in the seated position using a mercury sphygmomanometer, and the average of three readings was used for analyses.Anthropomorphic parameters such as height, weight, and waist circumference were measured. Body mass index (BMI) was calculated as weight/height , high-density lipoprotein-cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). Resting heart rate (RHR) was measured and calculated from electrocardiogram recordings after subjects acclimated to the hospital setting for ≥30 min and were in the supine position for ≥5 min.Sleep duration was elicited by the question “How many hours of sleep have you gotten on an average night in the preceding 3 months?” Response categories were ≤5, 6, 7, 8, and ≥9 h. Sleep duration in 2006–2007 and 2010–2011 was used to determine changes in sleep duration over the two exposure periods. To calculate change, baseline sleep duration (2006–2007) was subtracted from the sleep duration reported at follow-up (2010–2011). Decreased sleep was defined as a decrease of ≥2 h in sleep duration; whereas, increased sleep was defined as an increase of ≥2 h in sleep duration. Changes in sleep duration with the range of 0–1 h between 2006 and 2010 surveys was not considered to be different and classified as ‘no change in sleep duration’. For these stable sleepers, average sleep duration was calculated and categorized into five levels: ≤5.5, 6.0–6.5, 7.0, 7.5–8.0 and ≥8.5 h.In addition, participants were asked, “Do you generally snore when you sleep?” Response alternatives were “yes” and “no.”Participants were followed up by face-to-face interviews at every 2-year routine medical examination until December 31, 2015, or to the event of interest or death. Follow-ups were performed by trained physicians who were blinded to baseline data. MetS was diagnosed when a participant had three or more of the following components: 1) Waist circumference ≥90 cm for men or ≥80 cm for women; 2) TG ≥1.7 mmol/l; 3) HDL-C <1.03 mmol/l for men or <1.30 mmol/l for women; 4) SBP/DBP ≥130/85 mmHg or current use of antihypertensive medications; 5) FBG ≥5.6 mmol/l, previous diagnosis of type 2 diabetes, or current use of oral hypoglycemic agents or insulinContinuous variables were expressed as means ± standard deviations; whereas, categorical variables were expressed as percentages. We compared parameters according to each sleep duration group. One-way analysis of variance (ANOVA) was used for non-paired samples of normally distributed parameters and the Kruskal-Waillis test was applied for non-parametric variables. A Chi-squared test was applied to compare categorical variables. A multivariate analysis was performed using two models: Model 1 was adjusted for age, sex, and sleep duration at baseline; Model 2 included Model 1 parameters plus monthly income per family member, education level, marital status, smoking status, drinking status, physical activity, BMI, snoring status and RHR; We used Cox proportional hazards modeling to calculate the hazard ratio (HR) and 95% confidence interval (CI) of MetS, using the group with persistent 7-h sleep duration as a reference category. Person-years were calculated from the date of the 2010 survey was conducted to the date when MetS was detected , date of death or date of participating in the last interview in this analysis, whichever came first. Further, as individuals with major fatal diseases could impact our assessment of sleep duration and future MetS risk, we conducted three sensitivity analyses to test the robustness of our findings by repeating our aforementioned analysis and excluding individuals with stroke, myocardial infarction and cancer, respectively. Statistical analysis was performed using SAS 9.3 statistical software .How to cite this article: Song, Q. et al. Changes in sleep duration and risk of metabolic syndrome: the Kailuan prospective study. Sci. Rep.6, 36861; doi: 10.1038/srep36861 (2016).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


The purpose of this study was to identify suitable molecular targets for tumor-specific imaging of pancreatic adenocarcinoma.n = 9), pancreatic (n = 137), and periampullary (n = 28) adenocarcinoma.The expression of eight potential imaging targets was assessed by the target selection criteria (TASC)—score and immunohistochemical analysis in normal pancreatic tissue , epithelial growth factor receptor (EGFR), and urokinase plasminogen activator receptor (uPAR) showed a significantly higher  expression in pancreatic adenocarcinoma compared to normal pancreatic tissue and were confirmed by the TASC score as promising imaging targets. Furthermore, these biomarkers were expressed in respectively 88 %, 71 %, 69 %, and 67 % of the pancreatic adenocarcinoma patients.Integrin αvβ6, CEA, EGFR, and uPAR are suitable targets for tumor-specific imaging of pancreatic adenocarcinoma.The results of this study show that integrin α Pancreatic adenocarcinoma currently ranks the fourth leading cause of cancer-related death in the Western world, with a 5-year survival rate of less than 5 % . Radicalvβ6, carcinoembryonic antigen (CEA), epithelial growth factor receptor (EGFR), human epidermal growth factor receptor (HER2), urokinase plasminogen activator receptor (uPAR), or vascular endothelial growth factor receptor 2 (VEGFR2) among others , EGFR, epithelial cell adhesion molecule (EpCAM), HER2, uPAR, and VEGFR2 as molecular targets for tumor-targeted imaging of pancreatic adenocarcinoma patients. The primary endpoint of this study was to evaluate the ability of these markers to distinguish between normal pancreatic tissue and pancreatic and periampullary adenocarcinoma by performing immunohistochemistry on surgical specimen of these malignancies and normal pancreatic tissue obtained adjacent to the tumor. In addition, these biomarkers were judged on the Target Selection Criteria (TASC) proposed by Van Oosten et al. [Therefore, the aim of this study was to explore the suitability of integrin αn et al. .Medical records and pathology specimens of 137 patients with pancreatic ductal adenocarcinoma and 28 patients with periampullary adenocarcinoma who underwent pancreatic surgery at Leiden University Medical Center (LUMC) between June 2002 and July 2012 were retrospectively reviewed. Periampullary adenocarcinoma were included to assess the potential of tumor-specific imaging targets to visualize every pancreatic head mass, since preoperative differentiation between pancreatic, distal bile duct, ampullary, and duodenal adenocarcinoma can be challenging . For thevβ6, antigen retrieval was performed with 0.4 % pepsin incubation for 10 min at 37 °C. CEA staining did not require antigen retrieval. Immunohistochemical staining was performed by incubating tissue microarrays overnight with antibodies against VEGFR2 , EpCAM , c-MET , CEA , EGFR , integrin αvβ6 , HER2 , and uPAR  all at room temperature [vβ6, CEA, cMET, EGFR, EpCAM, HER2, uPAR, and VEGFR respectively, except for the CEA antibody  that was also sensitive to CEA-like proteins  and the uPAR antibody (ATN-615) that also recognizes the soluble form of uPAR suPAR [Tissue microarrays (TMAs) of tumor and normal tissues were constructed to perform uniform and simultaneous immunohistochemical stainings to limit intra-assay variations. Formalin-fixed paraffin-embedded tissue blocks of the primary tumor were collected from the archives of the Pathology Department. A single representative block was selected for each patient based on hematoxylin-eosin-stained sections. From each donor block, triplicate 2.0-mm cores were punched from areas with clear histopathological tumor representation and transferred to a recipient TMA block using the TMA Master . From each completed TMA block and normal pancreatic tissue block, 5-μm sections were sliced. The sections were deparaffined in xylene and rehydrated in serially diluted alcohol solutions, followed by demineralized water according to standard protocols. Endogenous peroxidase was blocked by incubation in 0.3 % hydrogen peroxide in phosphate-buffered saline (PBS) for 20 min. For EpCAM, c-MET, HER2, and uPAR staining antigen retrieval was performed by heat induction at 95 °C using PT Link  with a low-pH Envision FLEX target retrieval solution . VEGFR staining required antigen retrieval with high-pH Envision FLEX target retrieval solution . For staining of EGFR and integrin αperature , 23. AllAR suPAR . NegativAR suPAR . Evaluatin vivo (2 points); enzymatic activity (1 point); and target-mediated internalization (1 point). All biomarkers were granted points for the seven characteristics and a total score of 18 or higher indicated that the biomarker is potentially suitable for tumor-targeted imaging in vivo [The TASC score is based on granting points for the following seven characteristics of suitable molecular targets: extracellular protein localization ; diffuse upregulation through tumor tissue (4 points); tumor-to-healthy cell (T/N) ratio ; high percentage upregulation in patients ; previous imaging success  in vivo . Whereast test. In all tests, results were considered statistically significant at the level of p < 0.05.The statistical analysis was performed using SPSS version 23.0 software  and GraphPad Prism 6 . Interobserver variation of immunohistochemical results was analyzed using Cohen’s kappa coefficient, and >0.8 was considered as acceptable. Baseline characteristics between groups were analyzed using chi-squared test for categorical data. Immunohistochemistry staining intensity in normal pancreatic tissue was compared to pancreatic and periampullary adenocarcinoma using the independent Student’s p < 0.001), positive surgical margins , vascular invasion , perineural invasion , and received more often adjuvant therapy .In total, 165 patients were included, whereof 137 and 28 with pancreatic and periampullary adenocarcinoma, respectively  for CEA, 159 (96 %) for cMET, 156 (95 %) for EGFR, 151 (92 %) for EpCAM, 152 (92 %) for HER2, 155 (94 %) for VEGFR2, and 152 (92 %) for uPAR. The missing cases were due to staining artifacts, excessive necrotic tissue, or unacceptable tissue loss during the staining procedure. The molecular markers showed mainly membranous and cytoplasmic immunoreactivity in pancreatic and periampullairy adenocarcinoma cells; CEA and uPAR also showed stromal immunoreactivity , CEA , EGFR , and uPAR  was significantly higher compared to normal pancreatic tissue , CEA (p < 0.001), and VEGFR2 (p = 0.045) staining intensity were significantly higher.Of the 165 pancreatic and periampullary adenocarcinoma specimens collectively present on the TMA, 159 specimens (96 %) could successfully be microscopically quantified for integrin αma Table  and inteR2, 155 9 % for VEes Table . In addiR2, 155 9 % for VEvβ6 and/or CEA were expressed in 99 % of the patients and 64 % of the cases expressed both integrin αvβ6 and CEA, suggesting that the combination of both targets would be a promising approach for tumor-specific imaging. In periampullary adenocarcinoma, the most promising combination was CEA and EGFR, whereas all cases expressed either CEA and/or EGFR. In addition, integrin αvβ6 and/or CEA were expressed in 96 % of the cases.The combined expression of two biomarkers was evaluated to assess their potential as a dual target for tumor-specific imaging Table . In pancvβ6 (20 points), CEA (20 points), uPAR (19 points), cMET (18 points), and EGFR (18 points) were considered suitable targets for tumor-specific imaging of pancreatic adenocarcinoma according the TASC score. For tumor-specific imaging of periampullary adenocarcinoma, VEGFR2 (21 points), CEA (20 points), cMET (19 points), EGFR (18 points), and integrin αvβ6 (18 points) were categorized as potential targets by the TASC scoring system.The TASC score was calculated for all molecular markers evaluated in this study Tables  and 4. Ivβ6, CEA, EGFR, and uPAR are significantly upregulated in pancreatic adenocarcinoma compared to healthy pancreatic tissue and suggest that these biomarkers are promising targets for tumor-specific contrast agent development. By combining individual biomarkers in dual biomarker panels, the coverage of patients was increased: in pancreatic adenocarcinoma, considering almost the complete population expressed either integrin αvβ6 and/or CEA. Furthermore, the TASC score confirmed the potential of integrin αvβ6, CEA, EGFR, and uPAR as suitable targets for tumor-specific imaging.Tumor-specific intraoperative imaging is a rapidly emerging field that holds great promise to reduce tumor-positive resection margin rates in oncologic pancreatic surgery . Howevervβ6 (85–100 %), cMET (82–100 %), EGFR (36–69 %), EpCAM (56–78 %), HER2 (16–69 %), and VEGFR2 (64–93 %) in pancreatic adenocarcinoma are consistent with our results [vβ6, CEA, and uPAR has been described previously in compliance with our results, as very low or undetectable in normal pancreatic tissue, which would translate to a favorable tumor-to-background ratio when used for imaging purposes [Previous reports regarding the expression of integrin α results –47. Prec results , 35, 48. results , 40–42. purposes . EGFR anpurposes , 42, 49.purposes .vβ6 , CEA , EGFR , EpCAM (323A3), and uPAR (ATN-615) react on the extracellular epitopes of their analogues and have been described for use on intact protein [The results of this study are posed by limitations inherent to immunohistochemical analysis, such as variation in the quality of the primary antibodies, immunohistochemical staining techniques, scoring criteria, paraffin impregnation, surgical specimen fixation delay, or diversity in the ethnic distribution of the study population , 51. In  protein . The latvβ6, and has extensively been studied for imaging objectives [de novo developed imaging probes are autoquenched fluorescent probes, such as ProSense, that convert from a nonfluorescent to fluorescent state by proteolytic activation of lysosomal cysteine or serine proteases, hence the value of including enzymatic activity in by the TASC score [The TASC score identified cMET as a promising imaging target for pancreatic adenocarcinoma, whereas cMET did not significantly differentiate between healthy and malignant pancreatic tissue in our hands. These results suggest that the TASC score still experiences teething trouble and needs further validation and adaptation, since distinguishing between normal and malignant tissue is considered the cornerstone of surgical oncology. Various therapeutic antibodies have been investigated in preclinical models for imaging of cancer, including cetuximab, panitumumab, and bevacizumab –56. Humajectives , 59. In SC score . FurtherDespite the previously mentioned limitations, this study was to the best of our knowledge the first study to assess the ability of potential targets for the image-guided surgery of pancreatic adenocarcinoma to distinguish between normal and malignant pancreatic tissue in a relatively large cohort of patients with pancreatic adenocarcinoma using the TASC score. In addition, this study was also able to investigate the expression of potential imaging targets in periampullary adenocarcinoma. The latter is of added value since the histological origin of pancreatic head masses is often unknown in wait of pancreatic surgery. Furthermore, this study was to our knowledge the first to describe the combined expression of potential imaging targets to facilitate future development of dual-labeled imaging probes; however, these dual-purpose agents present additional hurdles in development and clinical translation that are beyond the scope of this article before their potential is fully realized . Moreovevβ6 and CEA would be able to detect tumor cells in 99 % of all pancreatic cancer patients.In conclusion, tumor-targeted intraoperative imaging of pancreatic adenocarcinoma has great potential to improve pancreatic surgery , 62. How


Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts. Recognizing these entities has become a hot topic in clinical natural language processing (NLP), and a large number of traditional machine learning methods, such as support vector machine and conditional random field, have been deployed to recognize entities from clinical texts in the past few years. In recent years, recurrent neural network (RNN), one of deep learning methods that has shown great potential on many problems including named entity recognition, also has been gradually used for entity recognition from clinical texts.In this paper, we comprehensively investigate the performance of LSTM (long-short term memory), a representative variant of RNN, on clinical entity recognition and protected health information recognition. The LSTM model consists of three layers: input layer – generates representation of each word of a sentence; LSTM layer – outputs another word representation sequence that captures the context information of each word in this sentence; Inference layer – makes tagging decisions according to the output of LSTM layer, that is, outputting a label sequence.Experiments conducted on corpora of the 2010, 2012 and 2014 i2b2 NLP challenges show that LSTM achieves highest micro-average F1-scores of 85.81% on the 2010 i2b2 medical concept extraction, 92.29% on the 2012 i2b2 clinical event detection, and 94.37% on the 2014 i2b2 de-identification, which is considerably competitive with other state-of-the-art systems.LSTM that requires no hand-crafted feature has great potential on entity recognition from clinical texts. It outperforms traditional machine learning methods that suffer from fussy feature engineering. A possible future direction is how to integrate knowledge bases widely existing in the clinical domain into LSTM, which is a case of our future work. Moreover, how to use LSTM to recognize entities in specific formats is also another possible future direction. With rapid development of electronic medical record (EMR) systems, more and more EMRs are available for researches and applications. Entity recognition, one of the most primary clinical natural language processing (NLP) tasks, has attracted considerable attention. As a large number of various types of entities widely exist in clinical texts, studies on entity recognition from clinical texts cover clinical entity recognition, clinical event recognition, protected health information recognition (PHI), etc. Compared to entity recognition in the newswire domain, studies on entity recognition in the clinical domain are slower initially.The early entity recognition systems in the clinical domain are mainly rule-based, such as MedLEE , SymTextIn recent years, deep learning, which has advantages in feature engineering, has been widely introduced into various fields, such as image processing, speech recognition and NLP, and has shown great potential. In the case of NLP, deep learning has been deployed to tackle machine translation , relatioIn this study, we comprehensively investigate entity recognition from clinical texts based on deep learning. Long-short term memory (LSTM), a representative variant of one type of deep learning method , is depThe following sections are organized as: section 2 introduces RNN in detail, experiments and results are presented in section 3, section 4 discusses the experimental results and section 5 draws conclusions.Entity recognition is usually treated as a sequence labeling problem, which can be modeled by RNN. Instead of traditional RNN, we used Long short-term memory (LSTM) , 42, a vt that takes xt, ht-1 and ct-1 as input and produces ht and ct via the following formulas:A LSTM unit is composed of three multiplicative gates: an input gate, a forget gate and an output gate, which control the proportion of input information transferred to a memory cell, the proportion of historical information from the previous state to forget, and the proportion of output information to pass on to the next step respectively. Fig. σ is the element-wise sigmoid function, ☉is the element-wise product, it, ft and ot are the input, forget, and output gates, ct is the cell vector, Wi, Wf, Wc, Wo  are the weight matrices for input xt, hidden state ht and memory cell ct respectively, and bi, bf, bc and bo denote the bias vectors.where The representation of a word is generated from the following two aspects: token-level and character-level, which capture context information and morphological information of the word respectively. The token-level representation is usually pre-trained by neural language models, such as continuous bag-of-words (CBOW) and skip-gram , on a las = w1w2…wn with each word wt (1 ≤ t ≤ n) represented by xt , the bidirectional LSTM takes a sequence of word representations x = x1x2…xn as input and produces a sequence of context representations h = h1h2…hn, where ht = T (1 ≤ t ≤ n) is a concatenation of outputs of both forward and backward LSTMs.A bidirectional LSTM is used to generate context representation at every position. Given a sentence D = {(xi)| i = 1,…,m} (yi), all parameters of CRF (θ) are estimated by maximizing the following log-likelihood function over D (only 1st order is considered here):Conditional random field (CRF) is employed to predict a label sequence from a sequence of context representations. Given a training set whereY(xi) denotes the set of possible label sequences for xi) a CRF-based system using rich features (denoted by CRF); 2) a LSTM-based system only using token-level word representations in the input layer (denoted by LSTM-BASELINE), then compare them with the LSTM-based systems using token-level word representations and two different types of character-level word representations. Moreover, we also compare the LSTM-based systems with other state-of-the-art systems. Three benchmark datasets from three clinical NLP challenges: i2b2 (the Center for Informatics for Integrating Biology & the Beside) 2010, 2012 and 2014 are used to evaluate the performance of all systems. Both 2010 and 2012 i2b2 NLP challenges have a subtask of clinical entity recognition, and the 2014 i2b2 NLP challenge have a subtask of PHI recognition.Three types of clinical entities, namely problem, test and treatment, require to be recognized in the 2010 i2b2 NLP challenge, while six types of clinical entities, namely problem, test, treatment, department, evidential and occurrence, in the 2012 i2b2 NLP challenge. In the 2014 i2b2 NLP challenge, seven types of PHI need to be recognized. The detailed statistics of the entity recognition datasets of the three challenges are listed in Table The performances of all systems are measured by micro-averaged precision (P), recall (R) and F1-score (F) under different criteria, which are calculated by the official evaluation tools provided by the organizers of the challenges. A brief introduction of the evaluation criteria for the three entity recognition tasks is presented in Table Sentence split: separate sentences using ‘\n’, ‘.’, ‘?’ and ‘!’.Tokenization: split sentences into tokens by blank characters at first, and then separate those tokens composed of more than two types of characters  into smaller parts that only contains only one type of characters. For example, “4/16/91CPT Code:” is split into “4/16/91CPT” and “Code:” at first, and then further separated into ‘4’, ‘/’, “16”, ‘/’, “91”, “CPT”, “Code” and ‘:’.Before training LSTM, we use the following two simple rules to split raw texts into sentences and tokenize the sentences:In this study, we use “BIOES”  to represent entities, and follow previous studies –35 to usLSTM only using token-level word representations as input  achieves F1-scores of 85.36% and 92.58% under “exact” and “inexact” criteria on the 2010 i2b2 challenge test set, F1-scores of 92.20% and 87.74% under “span” and “type” criteria on the 2012 i2b2 challenge test set, and F1-scores of 93.30% and 96.05% under “exact” and “token” criteria on the 2014 i2b2 challenge test set, as shown in Table When one type of character-level word representations  ) achieveIn recent months, a few studies on deep learning for entity recognition from clinical text are also proposed. For example, Abhyuday et al. proposed two RNN-based models for medical event detection on their own annotated dataset, one of which recognizes medical event detection as a classification problem and the other one as a sequence labeling problem , 49. BotIn this study, we comprehensively investigate the performance of recurrent neural network  on clinical entity recognition and protected health information (PHI) recognition. Experiments on the 2010, 2012 and 2014 i2b2 NLP challenge corpora prove that 1) LSTM outperforms CRF; 2) By introducing two types of character-level word representations into the input layer of LSTM, LSTM is further improved; 3) the final LSTM-based system is competitive with other state-of-the-art systems. Furthermore, we also point out two possible directions for further improvement.


In this paper, we derive an anisotropic Picone identity for the anisotropic Laplacian, which contains some known Picone identities. As applications, a Sturmian comparison principle to the anisotropic elliptic equation and an anisotropic Hardy type inequality are shown. If \doc,…,n) in  and  If S. Thus (b) If etc. Given a domain Let us address anisotropic Sobolev spaces; see Adams , Lu 28]28], TroiWe will show a Sturmian comparison principle to the anisotropic elliptic equation by Theorem Letandbe two continuous functions within the bounded domain Ω. Assume that there exists a positive functionsatisfyingThen any nontrivial solutionvto the following anisotropic elliptic equation: must change sign.v to , , v to  in  and notiIfthen, forit follows thatLet ,…,n) in . Recall 1|xi| in  that 3.9 Putting , 3.10\dox. Hence  is prove


Mindfulness meditators often show greater efficiency in resolving response conflicts than non-meditators. However, the neural mechanisms underlying the improved behavioral efficiency are unclear. Here, we investigated frontal theta dynamics—a neural mechanism involved in cognitive control processes—in long-term mindfulness meditators. The dynamics of EEG theta oscillations (4–8 Hz) recorded over the medial frontal cortex (MFC) were examined in terms of their power (MFC theta power) and their functional connectivity with other brain areas (the MFC-centered theta network). Using a flanker-type paradigm, EEG data were obtained from 22 long-term mindfulness meditators and compared to those from 23 matched controls without meditation experience. Meditators showed more efficient cognitive control after conflicts, evidenced by fewer error responses irrespective of response timing. Furthermore, meditators exhibited enhanced conflict modulations of the MFC-centered theta network shortly before the response, in particular for the functional connection between the MFC and the motor cortex. In contrast, MFC theta power was comparable between groups. These results suggest that the higher behavioral efficiency after conflicts in mindfulness meditators could be a function of increased engagement to control the motor system in association with the MFC-centered theta network. To optimize behavior, the brain is thought to monitor the presence of competing information and resolve the conflict. This cognitive process has been studied with various cognitive tasks, such as the Stroop, the Simon and the Flanker tasks, in which interference during the response selection stage causes response conflict. Converging evidence suggests that the medial frontal cortex (MFC), which included the anterior cingulate cortex (ACC), plays a significant role in the processing of response conflicts  were recruited through mailing lists and flyers distributed at various meditation centers. Inclusion criteria were at least 5 years of meditation practice, with a practice frequency of at least three times per week during the last 3 months. On average, meditators had 13.1 years (SD = 5.9) of meditation experience and meditated 247.8 min (SD = 104.9) per week. Twenty-five healthy matched controls of the same gender and age  were recruited through advertisements in the University Medical Center of Freiburg. Participants in the control group had no prior experience of contemplative practice including meditation, Tai Chi, Qi Gong and Yoga. The maximum age was set at 50 years for all participants. Further exclusion criteria were a history of psychiatric conditions, neurological diseases and visual impairment that cannot be corrected by means of visual aid.One participant of each group who had a high ratio of incorrect responses (greater than three standard deviations from the group mean) and three participants (two meditators and one control), who had high EEG artifacts (see below), were excluded from the analysis. Thus, we compared 22 long-term mindfulness meditators  and four controls (one left-handed and three mixed-handed). Three controls and one meditator did not report their handedness. This study was approved by the ethics committee of the University Medical Center of Freiburg. All participants gave written informed consent in accordance with the Declaration of Helsinki. As listed in Table Participants performed the flanker-type ANT Figure  with conParticipants held a two-button computer mouse with both hands and each thumb was placed on one mouse button. A fixation cross at the center of the screen was always displayed. After a random period varying between 400 ms and 1600 ms, a cue (an asterisk) appeared for 100 ms in one of the following positions: above or below the fixation cross , in the center (center-cue), or not at all (no-cue). Five-hundred milliseconds after the cue stimulus onset, a target arrow appeared for a maximum duration of 1700 ms either above or below the fixation cross. The target arrow was horizontally surrounded by two flanker arrows on each side, which pointed either in the same direction  or in the opposite direction  as the central arrow. Participants were asked to press either their left or right thumb as fast and as accurately as possible depending on the direction of the central target arrow. The duration of each trial was 4000 ms. After a 24-trials practice, participants had to perform three blocks of trials. Each block comprised 96 pseudo-randomized trials that consisted of 48 congruent and 48 incongruent trials, one third of which were either no-cue, center-cue, or spatial-cue trials (32 each). Since the present study focused on response conflict effects, the trials were only analyzed according to congruency, which allow the estimation of an individual conflict effect irrespective of the particular cueing condition  and 92.4% trials  were analyzed for congruent and incongruent conditions, respectively.EEGs were recorded with a 64-channel DC-EEG amplifier using active electrodes  without applying low cut-off and notch filters. An initial reference was placed at FPz and sampling rate was set at 500 Hz. One channel EOG was recorded to monitor ocular movements. Using Matlab  and EEGLAB toolbox ver. 13 , where f is the frequency and t is the time point at 125 Hz. From the resulting complex number, we estimated power as p = real[z]2 + imag[z]2 for each frequency. In order to minimize sensitivity to noisy trials, each trial was normalized by dividing each frequency by the mean spectral power of the frequency over the full length of the segment for each participant .All EEG segments were converted to current source density (CSD) using the method described by Kayser and Tenke , which if,t) = angle[z] and used to compute inter-channel phase synchronization (ICPS) between two channels. ICPS is defined as N is the number of trials and θ is the phase angle difference between two given electrodes, φ1 − φ2. ICPS ranges from 0 to 1 and values close to 1 indicate higher phase synchrony over trials between two electrodes.Instantaneous phase angle was defined as φ as a within-subject factor and group  as a between-subject factor. For the analyses of MFC-centered theta network, first, the effect on ICPSs of selected electrodes was tested using a 3 × 2 × 2 repeated measures ANOVA with electrode  and congruency  as within-subject factors and group  as a between-subject factor. Second, to further specify the functional connections, each pair of electrodes was separately subjected to a 2 × 2 repeated measures ANOVA with congruency  as a within-subject factor and group  as a between-subject factor. These follow up ANOVAs are confirmative analyses of the effects found in the main ANOVA to highlight which connection shows the most prominent effect, rather than determining significant effects per se.Effects of conflict modulation and group on RT, error rate (ER) and MFC theta power were separately tested using a 2 × 2 repeated measures analysis of variance (ANOVA) with congruency for both RT  = 333.41, p < 0.001, η2 = 0.886) and ER  = 41.87, p < 0.001, η2 = 0.493). This conflict effect showed no differences between groups in terms of RTs  = 0.077, p = 0.783, η2 = 0.002) and a non-significant congruency × group interaction  = 1.535, p = 0.222, η2 = 0.034). In contrast to RTs, fewer ERs were found among meditators  = 4.416, p = 0.041, η2 = 0.093) and congruency × group interaction  = 5.263, p = 0.027, η2 = 0.109). Post hoc comparisons showed that the group difference is mainly due to incongruent trials  than congruent trials . Therefore, these results indicate that meditators responded more accurately than controls irrespective of RTs, especially after conflicts  than incongruent trials  as reflected by significant main effects of F = 14.192, p < 0.001, η2 = 0.248) and in response-locked epochs  = 56.841, p < 0.001, η2 = 0.569). Although meditators showed higher power in congruent  and incongruent trials , we found no other significant main effects or interactions indicating group difference for both stimulus- and response-locked epochs .Time-frequency power plots show increased theta power (4–8 Hz) during incongruent compared to congruent trials within 400–600 ms after target stimulus onset (stimulus-locked epochs) and between −250 ms and −150 ms before response onset  revealed a significant main effect of congruency  = 10.639, p = 0.002, η2 = 0.198), showing an overall enhanced synchrony during incongruent  compared to congruent trials . Furthermore, we found a significant interaction of congruency × group  = 5.049, p = 0.030, η2 = 0.105). No other main effects or interactions were significant . Additionally, lateralization effect was also examined by comparing ICPSs between FCz–F5 and FCz–F6 and between FCz–CP3 and FCz–CP4 for each congruent and incongruent condition and the results showed no significant effects .The congruency × group ANOVAs were performed on each pair of electrodes, i.e., FCz–F5/6, FCz–CP3/4, FCz–P2. A significant main effect of congruency was observed on FCz–CP3/4  = 10.263, p = 0.003, η2 = 0.193) and FCz–P2  = 5.199, p = 0.028, η2 = 0.108; Figure F = 2.244, p = 0.141, η2 = 0.050). The congruency × group effect was observed on FCz–CP3/4  = 5.230, p = 0.027, η2 = 0.108), indicating that ICPS is comparable between groups during congruent trials  but meditators exhibited considerable enhanced synchrony compared to controls during incongruent trials . No other main effects or interactions were significant . These results indicate conflict modulations by FCz–CP3/4 and FCz-P2 synchronies. Furthermore, meditators exhibited an increased FCz–CP3/4 synchrony after conflicts, as compared to controls.To further specify the significant effects, follow-up electrode × congruency × group ANOVA revealed a significant main effect of electrode  = 14.940, p < 0.001, η2 = 0.258), indicating the highest synchrony between FCz and CP3/4  followed by FCz–P2  and FCz–F5/6 . No other main effects or interactions were significant .The same analysis was applied for ICPS around response onset (average between −50 ms and 50 ms of response-locked epochs). The congruency  and lateralization  as within-subject factors and group  as a between-subject factor. It revealed a significant main effect of lateralization  = 21.470, p < 0.001, η2 = 0.333), indicating that ICPS between FCz and CP3/4 is enhanced on contralateral  compared to ipsilateral areas . These results indicate that FCz–CP3/4 synchrony around response onset is specific to the responding hand.To test whether the strongest ICPS over the motor cortex (FCz–CP3/4) is dependent on the responding hand, we further conducted a repeated measures ANOVA with congruency × group ANOVA around these peak activities , revealed no significant main effects or interactions , indicating the absence of conflict effects or group differences.Lastly, we quantified the stimulus-related effect on ICPS between FCz and PO. In contrast to the other theta phase synchronizations, FCz–PO is more closely aligned to the stimulus onset than to the response onset Figure , which sr = –0.271; p = 0.036, one-tailed; Figure Having found increased ICPS between FCz and CP3/4 and fewer ERs in meditators, we further examined whether the increased theta phase synchrony over the motor cortex was predictive of low ER. Indeed, correlation analysis revealed that, in comparison to congruent trials, individuals with higher increase of ICPS between FCz and CP3/4 after conflict generally showed lower ER . Thus, it seems unlikely that the group difference in response accuracy is specific to MFC theta power. While the mental state of meditation may exhibit higher levels of theta power  also showed no group effect (To conclude, mindfulness meditators showed efficient cognitive control after conflicts as reflected by fewer error responses irrespective of response timing. Furthermore, meditators showed enhanced conflict modulations of functional connection between the MFC and the motor cortex, while MFC theta power showed no difference compared to the control group. This pattern of results suggests that the selective enhancement of frontal theta dynamics might render efficient cognitive control over the motor system. Our study is consistent with the idea that the MFC-centered network is relevant to the involvement in resolving conflicts to avoid errors.SS designed and conducted the experiment. H-GJ analyzed the data. H-GJ, PM and SS interpreted the data and wrote the article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Human activity perturbs the natural, steady-state level of 14C. Combustion of fossil fuels and emission of pollutants derived from fossil feedstocks dilute 14C with “dead” 12C (the “Suess effect”), and nuclear weapons tests produce excess 14C (the “bomb effect”). Therefore, sources are often revealed by measurement of 14C in species of environmental concern, such as “greenhouse” gases , atmospheric particles, and toxic or carcinogenic organic compounds.In the atmosphere, radioactive 14C concentration has been declining, largely because of equilibration of atmospheric CO2 with carbonate in the world’s oceans. The changing distribution of “bomb” carbon in the ocean has been measured to investigate ocean circulation patterns [14C. Radiocarbon has already been exploited as an inherent tracer to investigate the dispersion of plumes from nuclear power plants [Atmospheric weapons testing peaked in the early 1960s, and, in 1963, the atmospheric radiocarbon level was about twice the natural level [r plants ,6,7.14C→12C) per minute; this corresponds to a 14C concentration of 5.9 ×1010 atoms of 14C/gC. Because AMS detects atoms rather than radioactive decays, it is far more sensitive than conventional counting methods.For many species of interest, the small amount of sample available requires the use of accelerator mass spectrometry (AMS) for radiocarbon analysis. A gram of modern carbon undergoes 13.56 radioactive disintegrations  by ionization efficiency and halflife, and precision by counting statistics and “machine reproducibility” (currently ~0.5% for mg-size samples). A sample-preparation technique developed at NBS [2 followed by a closed- system reduction to CO over hot Zn and reduction of CO to C on Fe wool. The procedure, giving an overall blank (chemistry and accelerator) of 1.9±0.4 μg contemporary carbon, has been used to measure samples containing <100 μg of carbon (RSE= 10%) [At present, the accuracy of AMS is limited by contamination during sample preparation, sensitivity   allowed 14C results for the PAH fraction.Finally, in a second Albuquerque study conducted in December 1985, we isolated the polycyclic aromatic hydrocarbon (PAH) fraction from fine-particle samples. The behavior of two PAH, dehydroabietic acid (DHA) and benzo(ghi)perylene (BGP), is shown in


In DCS-GWO, the grey wolves' positions are initialized by using the q-thresholding algorithm and updated by using the idea of GWO. Inheriting the global search ability of GWO, DCS-GWO is efficient in finding global optimum solution. The simulation results illustrate that DCS-GWO has better recovery performance than previous greedy pursuit algorithms at the expense of computational complexity.Distributed Compressed Sensing (DCS) is an important research area of compressed sensing (CS). This paper aims at solving the Distributed Compressed Sensing (DCS) problem based on mixed support model. In solving this problem, the previous proposed greedy pursuit algorithms easily fall into suboptimal solutions. In this paper, an intelligent grey wolf optimizer (GWO) algorithm called DCS-GWO is proposed by combining GWO and  If x is sparse, it can be recovered from much fewer samples. We can get the measurement signal y = Φx by projecting x onto the measurement matrix Φ ∈ Rm×n, where m ≪ n. Because m < n, it is an NP-hard problem to recover x from y. However, if k < m < n and Φ satisfies the Restrict Isometry Property (RIP) with order k, x can be perfectly recovered. Gaussian random matrix  ∈ Rn×J denotes the joint signal and Y =  ∈ Rm×J denotes the measurement signal. The DCS problem is to recover X from Y jointly by exploiting intersignal and intrasignal correlations. For the matrix X ∈ Rn×J, the set of indices corresponding to nonzero rows of X is the joint support set of X, which can be denoted as R(X)≜{1 ≤ i ≤ n∣Xi,: ≠ 0}. If there are no more than K nonzero rows in X, X is called jointly sparse and the joint sparsity is K.Suppose that there are  Rm×n as , 21. ThaThe JSM reflects the intersignal correlations and intrasignal correlations. There are mainly three JSMs.(1) JSM-1. JSM-1 is called sparse common support and innovation model .(3) Update Strategy. The update strategy of DCS-GWO inherits from GWO. In DCS-GWO, t denotes the current iteration. For l ∈ Lwolf, the lth wolf's position is updated according to the previous best three wolves' positions Ibest1t−1, Ibest2t−1, and Ibest3t−1 and its previous position Ilt−1. A parameter Cn is used to limit the set size where K < Cn < spark(Φ).lth wolf is updated as follows. In Step 1.1, a set U1 is formed according to the previous best three positions and the lth wolf's previous position, U1 = (Ibest1t−1∩Ibest2t−1∩Ibest3t−1) ∪ Ilt−1. In Step 1.2, if |U1| > Cn, randomly choose Cn − (Ibest1t−1∩Ibest2t−1∩Ibest3t−1) elements from Ilt−1 to form the set U2 and define the temporary position U = (Ibest1t−1∩Ibest2t−1∩Ibest3t−1) ∪ U2; if |U1| < Cn, randomly choose Cn − |U1| elements from S − U1 to form a set U3 and define the temporary position U = U1 ∪ U3. In Step 1.3, the temporary solution rec Xlt is estimated by using the least square method as The position of the lth wolf is updated by l ∈ {1,2,…, L}. After all wolves' positions are updated, we can update the best three wolves' positions according to the cost function.Lastly, the position of the (4) Stopping Criterion. In order to avoid too many iterations, we set a maximum allowed iteration number Nmax and a small positive number ε as stopping criterions. If f(Ibest1t) < ε or the number of iterations reaches Nmax, the iteration process is terminated.We summarize the DCS-GWO in O(LJmn). In each iteration, the main computational complexity lies in Steps 1.3 and 1.4 which, respectively, have upper limit value O(m3) + O(Jm2) and O(mK2) + O(JmK). Because K ≤ m, the computational complexity upper limit value of each iteration is O(m3) + O(Jm2). Because the total number of iterations is not more than LNmax, the computational complexity upper limit value of DCS-GWO is O(NmaxLm3) + O(NmaxLJm2) + O(LJmn). Obviously, as an intelligent optimizer algorithm, DCS-GWO has higher computational complexity than greedy pursuit algorithms. However, as swarm intelligence algorithm, it can run in parallel to reduce the running time.According to Nmax = max, L = 8, Cn = 0.8 m, and ε = 1e − 5. We use the following hypothesis in the simulation.In this section, the performance of DCS-GWO is compared with other algorithms that can solve the DCS problem based on JSM-3, including Joint OMP , Joint Sl2 norm. All signals follow the JSM-3 with the common sparsity Kc and innovation sparsity Kj. We assume that the innovation sparsity Kj is the same for all signals. For each signal, the support sets of common component and innovation component are random subsets of the set S = {1,2,…, n}. The nonzero coefficients of the common component and innovation component are randomly drawn from the standard i.i.d. In each experiment, 200 independent trials are conducted. In each trial, the signals and measurement matrix Φ are generated independently. Average Normalized Mean Squared Error (ANMSE), perfect recovery percentage, and average runtime are used to evaluate the algorithms. The ANMSE is defined asXi) Requirement for Measurements. In the first simulation, we compare the performance of all algorithms against the measurement number M changing from 50 to 90 with Step 10. Other parameters are fixed as N = 256, Kc = 15, Kj = 5, and J = 3.As From (2) Robustness against Common Sparsity. In the second simulation, we evaluate the performance of the algorithms against the common sparsity Kc changing from 1 to 15 with the step size 2. Other parameters are fixed as N = 256, M = 40, J = 3, and Kj = 3.Kc increases, the perfect recovery percentage decreases. We are more interested in at which sparsity the perfect recovery percentages drop below 1. The perfect recovery percentage of DCS-GWO starts to fall below 1 when Kc > 7; however, other algorithms already fall below 1 when Kc > 3. From As (3) Robustness against Innovation Sparsity. In the third simulation, we compare the performance of DCS-GWO with other algorithms against the innovation sparsity Kj changing from 0 to 7 with Step 1. Other parameters are set as N = 256, M = 40, J = 3, and Kc = 5.Kj, the perfect recovery percentage is declining, that is, because the increase of joint sparsity level influences the performance of all algorithms. The perfect recovery percentages of other algorithms start to fall below 1 when Kj > 2; our algorithm starts to fall below 1 until Kj > 5. As in As (4) Robustness against the Number of Signals. We compare our algorithm with other algorithms against the number of signals J changing from 2 to 6 with Step 1. Other parameters are set as N = 256, M = 40, Kc = 4, and Kj = 4.J > 5; however, our algorithm performs better than them when J ≤ 5.The perfect recovery percentages of Joint SP and Joint OMP are not influenced obviously by the increase of signals number, because both of them recover the signals one by one rather than jointly. They have better performance than our algorithms when J increases, the perfect recovery percentages of DCSFBP, DCS-GWO, and DCSSAMP decrease. As J > 2, the perfect recovery percentages of DCS-FBP and DCSSAMP already fall below 1; however, our algorithm starts to fall below 1 until J > 5. As As the number of signals From the above simulations, we can see that DCS-GWO has higher perfect recovery percentage and lower ANMSE than greedy pursuit algorithms. Next, we analyze the reason of DCS-GWO's better performance according to its structure.Cn − K indices corresponding to the rows of the temporal joint signal which have smallest row norm values are removed from the temporal support set. By this way, the previous wrong selected indices can be removed from the temporal support set.The main reason for DCS-GWO's better performance is its update mechanism. In Step 1.1, the common information of the best three grey wolves' positions is utilized to update all the grey wolves' positions. By this way, the previous obtained best information is preserved in the new grey wolves' positions. In Step 1.2, random perturbations are introduced into the grey wolves' positions. Benefitting from this, DCS-GWO can skip the local optimum position and guide the search in promising directions. In Steps 1.3 and 1.4, In contrast to DCS-GWO, greedy pursuit algorithms search the support set according to the gradient of , which iq-thresholding algorithm and updated by using the idea of GWO. Inheriting the global search ability of GWO, DCS-GWO overcomes greedy pursuit algorithms' shortcoming of easily falling into suboptimal solutions. The simulation results illustrate that DCS-GWO has higher perfect recovery percentage and lower ANMSE than other algorithms. DCS-GWO has higher computational complexity than other algorithms. However, as a swarm intelligence algorithm, it can compute in parallel to reducing the running time. In the future work, we will focus on developing effective update strategies to reduce the running time. Moreover, there are many more recent nature-inspired algorithms, such as Ant Lion Optimizer (ALO) [In this paper, an intelligent grey wolf optimizer algorithm is proposed to solve the DCS problem based on JSM-3. The positions of grey wolves are initialized by using the er (ALO) , Moth-Fler (ALO) , Whale Oer (ALO) , and Muler (ALO) . We will


By , there eNext, we give a direct local approximation theorem for the operators Forwe havewhereCis a positive constant, andare defined in (and (fined in  and 17)For\docuWe define the auxiliary operatorsx). From  and 6),,24\documence, by , we have(x)). By , we havefined in  and 17)24\documeFor any f belongs to Now, we study the rate of convergence of the operators Letandthen we havewhereis defined in =0 by . Finally;x)=0 by , 19), (, (\documλ and n.In this section, we give several graphs and numerical examples to show the convergence of n are shown in Figs. λ. Let entclass1pt{minima 1 - \cose^{x}$\en