Body mass index dependent metabolic syndrome in severe mental illness patients

The aim of this study was to evaluate the body mass index dependent metabolic syndrome in severe mental illness patients in Gorgan. A total of 267 severe mental illness patients took part in this study. The prevalence of metabolic syndrome in severe mental illness patients in different body mass index were 6.67, 24.09 and 53.06. There were significant differences in the mean of waist circumference, HDL-cholesterol, triglyceride and fasting blood glucose in subjects with metabolic syndrome in different body mass index when compared with subjects without metabolic syndrome (p<0.05). The prevalence of high fasting glucose, low high density lipoprotein-cholesterol, high triglyceride levels, high waist circumference and high blood pressure were 14.23, 38.57, 41.57, 32.96 and 5.24, respectively. It shows that high triglyceride levels (41.57) and Low HDL-cholesterol levels (38.57) were the most frequent characteristics in comparison to other metabolic components. Our results show that, 26.96, 31.08, 21.35, 15.35 and 5.25 of subjects had zero, one, two, three and four criteria for metabolic syndrome, respectively. These results show that the prevalence of metabolic syndrome in severe mental illness patients in Gorgan is increased with elevated body mass index. The results of this study suggest that mental illness patients are at risk of metabolic syndrome, when the rate of body mass index increases. Risk factors such as high triglyceride level and low HDL-cholesterol may play an important role in the occurrence of metabolic syndrome in severe mental illness patients. © 2015, Asian Network for Scientific Information. All rights reserved

Effects of Mentha pulegium water extract dipping on quality and shelf life of silver carp (Hypophthalmichthys molitrix) during superchilled storage

The effects of Mentha pulegium water extract dipping on quality and shelf life of silver carp during superchilled storage were investigated. Fish samples were treated with water extract of 1 and 3% M. pulegium, and then stored at -3 οC for 30 days. The control and the treated fish samples were analyzed periodically for chemical (pH, PV, TBA, TVB-N), and sensory characteristics. The results indicated that the effect of M. pulegium extract dipping on fish samples was to retain their good quality characteristics and extend the shelf life during superchilled storage, which was supported by the results of chemical and sensory evaluation analyses. In this respect, the sample supplemented with 3% water extract was more potent compared with the 1% one in extending the shelf life of fish fillets

Detection and determination of groundwater contamination plume using time-lapse electrical resistivity tomography (ERT) method

Abstract Protection of water resources from contamination and detection of the contaminants and their treatments are among the essential issues in the management of water resources. In this work, the time-lapse electrical resistivity tomography (ERT) surveys were conducted along 7 longitudinal lines in the downstream of the Latian dam in Jajrood (Iran), in order to detect the contamination resulting from the direct injection of a saltwater solution in to the saturated zone in the area. To investigate the pollutant quantities affecting the resistivity of this zone, the temperature and electrical conductivity measurement were carried out using a self-recording device during 20 days (before and after the injection). The results obtained from the selfrecording device measurements and ERT surveys indicated that in addition to the salt concentration changes in water, the resistivity changes in the saturated zone were dependent on other factors such as the lithology and absorption of contaminants by the subsurface layers. Furthermore, the expansion of contamination toward the geological trend, sedimentation, and groundwater flow direction of the area were shown

A Multi-Solver Scheme for Viscous Flows Using Adaptive Cartesian Grids and Meshless Grid Communication

This work concerns the development of an adaptive multi-solver approach for CFD simulation of viscous flows. Curvilinear grids are used near solid bodies to capture boundary layers, and stuctured adaptive Cartesian grids are used away from the body to fill the majority of the computational domain. An edge-based meshless scheme is used in the interface region to connnect the near-body and off-body codes. We show that the combination of a body-fitted grid near the surface coupled with an adaptive Cartesian grid system away from the surface leads to a highly efficient scheme with sharp feature resolution. The use of a meshless flow solver to interface the body-fitted and Cartesian grid systems leads to seamless grid communication without many of the complexities inherent in traditional Chimera overset grid interpolation schemes. The hierarchical structure of the nested Cartesian grids may be exploited to achieve multigrid convergence for steady problems and for use in dual-time stepping algorithms for unsteady problems. Results of two-dimensional steady airfoil calculations are presented. I

Identifying and explaining the farming system composition of agricultural landscapes: The role of socioeconomic drivers under strong biophysical gradients

In mountain landscapes, agricultural abandonment is taking place in the most vulnerable areas, while intensification increases in the most productive lands. These contrasting processes, which have different impacts on biodiversity and ecosystem services (BES), are related to changes in the farming system component of these landscapes. Farming systems are identified based on farmer’s decisions on, for example, type of crop and level of fertilizers, which represent the descriptors of farming systems and can be grouped into several dimensions (e.g. land use and intensity). Since obtaining this data at farm-level is often difficult, an alternative is to study the spatial combinations of farming systems at parish-level, i.e., Farming System Mixes (FSM), relying on agricultural census data. Other biophysical (e.g. climate, soil) and socioeconomic (e.g. labour, farmer’s age) variables, independent of farmers' decisions, represent the exogenous drivers of these decisions. The separation between descriptors and drivers is important to improve knowledge about what drives farmers' decisions regarding farming system choice, as these choices are often the focus of policies aiming the support of BES. In this study, we explored the underlying drivers of FSM and assessed the role of socioeconomic drivers, main target for policy makers, in a context of strong biophysical gradients. Biophysical drivers emerge as those that primarily discriminate between the FSM located in different topographic positions (valleys, mountains and plateau). In the situations where there is a greater range of productive choices available for farmers, such as in valleys, socioeconomic drivers assume a preponderant role on farming system choiceinfo:eu-repo/semantics/publishedVersio

An experimental study of the intrinsic stability of random forest variable importance measures

BACKGROUND: The stability of Variable Importance Measures (VIMs) based on random forest has recently received increased attention. Despite the extensive attention on traditional stability of data perturbations or parameter variations, few studies include influences coming from the intrinsic randomness in generating VIMs, i.e. bagging, randomization and permutation. To address these influences, in this paper we introduce a new concept of intrinsic stability of VIMs, which is defined as the self-consistence among feature rankings in repeated runs of VIMs without data perturbations and parameter variations. Two widely used VIMs, i.e., Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) are comprehensively investigated. The motivation of this study is two-fold. First, we empirically verify the prevalence of intrinsic stability of VIMs over many real-world datasets to highlight that the instability of VIMs does not originate exclusively from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. Second, through Spearman and Pearson tests we comprehensively investigate how different factors influence the intrinsic stability. RESULTS: The experiments are carried out on 19 benchmark datasets with diverse characteristics, including 10 high-dimensional and small-sample gene expression datasets. Experimental results demonstrate the prevalence of intrinsic stability of VIMs. Spearman and Pearson tests on the correlations between intrinsic stability and different factors show that #feature (number of features) and #sample (size of sample) have a coupling effect on the intrinsic stability. The synthetic indictor, #feature/#sample, shows both negative monotonic correlation and negative linear correlation with the intrinsic stability, while OOB accuracy has monotonic correlations with intrinsic stability. This indicates that high-dimensional, small-sample and high complexity datasets may suffer more from intrinsic instability of VIMs. Furthermore, with respect to parameter settings of random forest, a large number of trees is preferred. No significant correlations can be seen between intrinsic stability and other factors. Finally, the magnitude of intrinsic stability is always smaller than that of traditional stability. CONCLUSION: First, the prevalence of intrinsic stability of VIMs demonstrates that the instability of VIMs not only comes from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. This finding gives a better understanding of VIM stability, and may help reduce the instability of VIMs. Second, by investigating the potential factors of intrinsic stability, users would be more aware of the risks and hence more careful when using VIMs, especially on high-dimensional, small-sample and high complexity datasets

A tale of two targets: examining the differential effects of posterior cingulate cortex- and amygdala-targeted fMRI-neurofeedback in a PTSD pilot study

IntroductionReal-time fMRI-based neurofeedback (rt-fMRI-NFB) is a non-invasive technology that enables individuals to self-regulate brain activity linked to neuropsychiatric symptoms, including those associated with post-traumatic stress disorder (PTSD). Selecting the target brain region for neurofeedback-mediated regulation is primarily informed by the neurobiological characteristics of the participant population. There is a strong link between PTSD symptoms and multiple functional disruptions in the brain, including hyperactivity within both the amygdala and posterior cingulate cortex (PCC) during trauma-related processing. As such, previous rt-fMRI-NFB studies have focused on these two target regions when training individuals with PTSD to regulate neural activity. However, the differential effects of neurofeedback target selection on PTSD-related neural activity and clinical outcomes have not previously been investigated.MethodsHere, we compared whole-brain activation and changes in PTSD symptoms between PTSD participants (n = 28) that trained to downregulate activity within either the amygdala (n = 14) or the PCC (n = 14) while viewing personalized trauma words.ResultsFor the PCC as compared to the amygdala group, we observed decreased neural activity in several regions implicated in PTSD psychopathology – namely, the bilateral cuneus/precuneus/primary visual cortex, the left superior parietal lobule, the left occipital pole, and the right superior temporal gyrus/temporoparietal junction (TPJ) – during target region downregulation using rt-fMRI-NFB. Conversely, for the amygdala as compared to the PCC group, there were no unique (i.e., over and above that of the PCC group) decreases in neural activity. Importantly, amygdala downregulation was not associated with significantly improved PTSD symptoms, whereas PCC downregulation was associated with reduced reliving and distress symptoms over the course of this single training session. In this pilot analysis, we did not detect significant between-group differences in state PTSD symptoms during neurofeedback. As a critical control, the PCC and amygdala groups did not differ in their ability to downregulate activity within their respective target brain regions. This indicates that subsequent whole-brain neural activation results can be attributed to the effects of the neurofeedback target region selection in terms of neurophysiological function, rather than as a result of group differences in regulatory success.ConclusionIn this study, neurofeedback-mediated downregulation of the PCC was differentially associated with reduced state PTSD symptoms and simultaneous decreases in PTSD-associated brain activity during a single training session. This novel analysis may guide researchers in choosing a neurofeedback target region in future rt-fMRI-NFB studies and help to establish the clinical efficacy of specific neurofeedback targets for PTSD. A future multi-session clinical trial of rt-fMRI-NFB that directly compares between PCC and amygdala target regions is warranted

A silviculture-oriented spatio-temporal model for germination in Pinus pinea L. in the Spanish Northern Plateau based on a direct seeding experiment

Natural regeneration in Pinus pinea stands commonly fails throughout the Spanish Northern Plateau under current intensive regeneration treatments. As a result, extensive direct seeding is commonly conducted to guarantee regeneration occurrence. In a period of rationalization of the resources devoted to forest management, this kind of techniques may become unaffordable. Given that the climatic and stand factors driving germination remain unknown, tools are required to understand the process and temper the use of direct seeding. In this study, the spatio-temporal pattern of germination of P. pinea was modelled with those purposes. The resulting findings will allow us to (1) determine the main ecological variables involved in germination in the species and (2) infer adequate silvicultural alternatives. The modelling approach focuses on covariates which are readily available to forest managers. A two-step nonlinear mixed model was fitted to predict germination occurrence and abundance in P. pinea under varying climatic, environmental and stand conditions, based on a germination data set covering a 5-year period. The results obtained reveal that the process is primarily driven by climate variables. Favourable conditions for germination commonly occur in fall although the optimum window is often narrow and may not occur at all in some years. At spatial level, it would appear that germination is facilitated by high stand densities, suggesting that current felling intensity should be reduced. In accordance with other studies on P. pinea dispersal, it seems that denser stands during the regeneration period will reduce the present dependence on direct seeding