Cognitive regulation of emotions is a fundamental prerequisite for intact social functioning which impacts on both well being and psychopathology. The neural underpinnings of this process have been studied intensively in recent years, without, however, a general consensus. We here quantitatively summarize the published literature on cognitive emotion regulation using activation likelihood estimation in fMRI and PET (23 studies/479 subjects). In addition, we assessed the particular functional contribution of identified regions and their interactions using quantitative functional inference and meta-analytic connectivity modeling, respectively. In doing so, we developed a model for the core brain network involved in emotion regulation of emotional reactivity. According to this, the superior temporal gyrus, angular gyrus and (pre) supplementary motor area should be involved in execution of regulation initiated by frontal areas. The dorsolateral prefrontal cortex may be related to regulation of cognitive processes such as attention, while the ventrolateral prefrontal cortex may not necessarily reflect the regulatory process per se, but signals salience and therefore the need to regulate. We also identified a cluster in the anterior middle cingulate cortex as a region, which is anatomically and functionally in an ideal position to influence behavior and subcortical structures related to affect generation. Hence this area may play a central, integrative role in emotion regulation. By focusing on regions commonly active across multiple studies, this proposed model should provide important a priori information for the assessment of dysregulated emotion regulation in psychiatric disorders.
There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at http://msi-workgroups.sourceforge.net/ or http://Msiemail@example.com. Further, community input related to this document can also be provided via this electronic forum.
Abstract A non-linear poroelastic finite element model of the lumbar spine was developed to investigate spinal response during daily dynamic physiological activities. Swelling was simulated by imposing a boundary pore pressure of 0.25 MPa at all external surfaces. Partial saturation of the disc was introduced to circumvent the negative pressures otherwise computed upon unloading. The loading conditions represented a pre-conditioning full day followed by another day of loading: 8 h rest under a constant compressive load of 350 N, followed by 16 h loading phase under constant or cyclic compressive load varying in between 1000 and 1600 N. In addition, the effect of one or two short resting periods in the latter loading phase was studied. The model yielded fairly good agreement with in-vivo and in-vitro measurements. Taking the partial saturation of the disc into account, no negative pore pressures were generated during unloading and recovery phase. Recovery phase was faster than the loading period with equilibrium reached in only ∼3 h. With time and during the day, the axial displacement, fluid loss, axial stress and disc radial strain increased whereas the pore pressure and disc collagen fiber strains decreased. The fluid pressurization and collagen fiber stiffening were noticeable early in the morning, which gave way to greater compression stresses and radial strains in the annulus bulk as time went by. The rest periods dampened foregoing differences between the early morning and late in the afternoon periods. The forgoing diurnal variations have profound effects on lumbar spine biomechanics and risk of injury.
Background: Numerous studies have investigated the relationship between COX-2 8473 T > C polymorphism and cancer susceptibility, however, the results remain controversial. Therefore, we carried out the present meta-analysis to obtain a more accurate assessment of this potential association. Methods: In this meta-analysis, 79 case-control studies were included with a total of 38,634 cases and 55,206 controls. We searched all relevant articles published in PubMed, EMBASE, OVID, Web of Science, CNKI and Wanfang Data, till September 29, 2017. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to evaluate the strength of the association. We performed subgroup analysis according to ethnicity, source of controls, genotyping method and cancer type. Moreover, Trial sequential analysis (TSA) was implemented to decrease the risk of type I error and estimate whether the current evidence of the results was sufficient and conclusive. Results: Overall, our results indicated that 8473 T > C polymorphism was not associated with cancer susceptibility. However, stratified analysis showed that the polymorphism was associated with a statistically significant decreased risk for nasopharyngeal cancer and bladder cancer, but an increased risk for esophageal cancer and skin cancer. Interestingly, TSA demonstrated that the evidence of the result was sufficient in this study. Conclusion: No significant association between COX-2 8473 T > C polymorphism and cancer risk was detected.
The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA–DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18–85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at ( ).
This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit ; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the 1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces.
Background: The prevalence of class III obesity (body mass index [BMI]>= 40 kg/m(2)) has increased dramatically in several countries and currently affects 6% of adults in the US, with uncertain impact on the risks of illness and death. Using data from a large pooled study, we evaluated the risk of death, overall and due to a wide range of causes, and years of life expectancy lost associated with class III obesity. Methods and Findings: In a pooled analysis of 20 prospective studies from the United States, Sweden, and Australia, we estimated sex-and age-adjusted total and cause-specific mortality rates (deaths per 100,000 persons per year) and multivariable-adjusted hazard ratios for adults, aged 19-83 y at baseline, classified as obese class III (BMI 40.0-59.9 kg/m(2)) compared with those classified as normal weight (BMI 18.5-24.9 kg/m(2)). Participants reporting ever smoking cigarettes or a history of chronic disease (heart disease, cancer, stroke, or emphysema) on baseline questionnaires were excluded. Among 9,564 class III obesity participants, mortality rates were 856.0 in men and 663.0 in women during the study period (19762009). Among 304,011 normal-weight participants, rates were 346.7 and 280.5 in men and women, respectively. Deaths from heart disease contributed largely to the excess rates in the class III obesity group (rate differences = 238.9 and 132.8 in men and women, respectively), followed by deaths from cancer (rate differences = 36.7 and 62.3 in men and women, respectively) and diabetes (rate differences = 51.2 and 29.2 in men and women, respectively). Within the class III obesity range, multivariable-adjusted hazard ratios for total deaths and deaths due to heart disease, cancer, diabetes, nephritis/nephrotic syndrome/nephrosis, chronic lower respiratory disease, and influenza/pneumonia increased with increasing BMI. Compared with normal-weight BMI, a BMI of 40-44.9, 45-49.9, 50-54.9, and 55-59.9 kg/m(2) was associated with an estimated 6.5 (95% CI: 5.7-7.3), 8.9 (95% CI: 7.4-10.4), 9.8 (95% CI: 7.4-12.2), and 13.7 (95% CI: 10.5-16.9) y of life lost. A limitation was that BMI was mainly ascertained by self-report. Conclusions: Class III obesity is associated with substantially elevated rates of total mortality, with most of the excess deaths due to heart disease, cancer, and diabetes, and major reductions in life expectancy compared with normal weight.
Meta-analysis is a method to obtain a weighted average of results from various studies. In addition to pooling effect sizes, meta-analysis can also be used to estimate disease frequencies, such as incidence and prevalence. In this article we present methods for the meta-analysis of prevalence. We discuss the logit and double arcsine transformations to stabilise the variance. We note the special situation of multiple category prevalence, and propose solutions to the problems that arise. We describe the implementation of these methods in the MetaXL software, and present a simulation study and the example of multiple sclerosis from the Global Burden of Disease 2010 project. We conclude that the double arcsine transformation is preferred over the logit, and that the MetaXL implementation of multiple category prevalence is an improvement in the methodology of the meta-analysis of prevalence.
Summary Background Patients with peritoneal metastatic colorectal cancer have reduced overall survival compared with patients with metastatic colorectal cancer without peritoneal involvement. Here we further investigated the effect of the number and location of metastases in patients receiving first-line systemic chemotherapy. Methods We analysed individual patient data for previously untreated patients enrolled in 14 phase 3 randomised trials done between 1997 and 2008. Trials were included if protocols explicitly pre-specified and solicited for patients with peritoneal involvement in the trial data collection process or had done a formal peritoneum-focused review of individual pre-treatment scans. We used stratified multivariable Cox models to assess the prognostic associations of peritoneal metastatic colorectal cancer with overall survival and progression-free survival, adjusting for other key clinical-pathological factors (age, sex, Eastern Cooperative Oncology Group (ECOG) performance score, primary tumour location [colon vs rectum], previous treatment, and baseline BMI). The primary endpoint was difference in overall survival between populations with and without peritoneal metastases. Findings Individual patient data were available for 10 553 patients. 9178 (87%) of 10 553 patients had non-peritoneal metastatic colorectal cancer (4385 with one site of metastasis, 4793 with two or more sites of metastasis), 194 (2%) patients had isolated peritoneal metastatic colorectal cancer, and 1181 (11%) had peritoneal metastatic colorectal cancer and other organ involvement. These groups were similar in age, ethnic origin, and use of targeted treatment. Patients with peritoneal metastatic colorectal cancer were more likely than those with non-peritoneal metastatic colorectal cancer to be women (565 [41%] of 1371 vs 3312 [36%] of 9169 patients; p=0·0003), have colon primary tumours (1116 [84%] of 1334 patients vs 5603 [66%]; p<0·0001), and have performance status of 2 (136 [10%] vs 521 [6%]; p<0·0001). We recorded a higher proportion of patients with mutated BRAF in patients with peritoneal-only (eight [18%] of 44 patients with available data) and peritoneal metastatic colorectal cancer with other sites of metastasis (34 [12%] of 289), compared with patients with non-peritoneal metastatic colorectal cancer (194 [9%] of 2230; p=0·028 comparing the three groups). Overall survival (adjusted HR 0·75, 95% CI 0·63–0·91; p=0·003) was better in patients with isolated non-peritoneal sites than in those with isolated peritoneal metastatic colorectal cancer. Overall survival of patients with two of more non-peritoneal sites of metastasis (adjusted HR 1·04, 95% CI 0·86–1·25, p=0.69) and those with peritoneal metastatic colorectal cancer plus one other site of metastasis (adjusted HR 1·10, 95% CI 0·89–1·37, p=0·37) was similar to those with isolated peritoneal metastases. Compared with patients with isolated peritoneal metastases, those with peritoneal metastases and two or more additional sites of metastasis had the shortest survival (adjusted HR 1·40; CI 1·14–1·71; p=0·0011). Interpretation Patients with peritoneal metastatic colorectal cancer have significantly shorter overall survival than those with other isolated sites of metastases. In patients with several sites of metastasis, poor survival is a function of both increased number of metastatic sites and peritoneal involvement. The pattern of metastasis and in particular, peritoneal involvement, results in prognostic heterogeneity of metastatic colorectal cancer. Funding None.
Parametric voxelwise analysis is a commonly used tool in neuroimaging, as it allows for identification of regions of effects in the absence of a strong a-priori regional hypothesis by comparing each voxel of the brain independently. Due to the inherent imprecision of single voxel measurements, spatial smoothing is performed to increase the signal-to-noise ratio of single-voxel estimates. In addition, smoothing compensates for imprecisions in anatomical registration, and allows for the use of cluster-based statistical thresholding. Smoothing has traditionally been applied in three dimensions, without taking the tissue types of surrounding voxels into account. This procedure may be suitable for subcortical structures, but is problematic for cortical regions for which grey matter often constitutes only a small proportion of the smoothed signal. New methods have been developed for cortical analysis in which voxels are sampled to a surface, and smoothing is restricted to neighbouring regions along the cortical grey matter in two dimensions. This procedure has recently been shown to decrease intersubject variability and bias of PET data. The aim of this study was to compare the variability, bias and test-retest reliability of volumetric and surface-based methods as they are applied in practice. Fifteen healthy young males were each measured twice using the dopamine D1 receptor radioligand [ C]SCH-23390, and analyses were performed at the level of individual voxels and vertices within the cortex. We found that surface-based methods yielded higher BP values, lower coefficient of variation, less bias, better reliability and more precise estimates of parametric binding. All in all, these results suggest that surface-based methods exhibit superior performance to volumetric approaches for voxelwise analysis of PET data, and we advocate for their use when a ROI-based analysis is not appropriate.