Metabolic Syndrome and Cardiovascular Risk Factors in a National Sample of Adolescent Population in the Middle East and North Africa: The CASPIAN III Study

Objective. The present study was designed to investigate the prevalence of different combinations of the metabolic syndrome (MetS) risk factors among a nationally representative sample of adolescents in the Middle East and North Africa (MENA). Methods. The study sample, obtained as part of the third study of the school-based surveillance system entitled CASPIAN III, was representative of the Iranian adolescent population aged from 10 to 18 years. The prevalence of different components of MetS was studied and their discriminative value was assessed by receiver operating characteristic (ROC) curve analysis. Results. The study participants consisted of 5738 students (2875 girls) with mean age of 14.7±2.4 years) living in 23 provinces in Iran; 17.4% of participants were underweight and 17.7% were overweight or obese. Based on the criteria of the International Diabetes Federation for the adolescent age group, 24.2% of participants had one risk factor, 8.0% had two, 2.1% had three, and 0.3% had all the four components of MetS. Low HDL-C was the most common component (43.2% among the overweight/obese versus 34.9% of the normal-weight participants), whereas high blood pressure was the least common component. The prevalence of MetS was 15.4% in the overweight/obese participants, the corresponding figure was 1.8% for the normal-weight students, and 2.5% in the whole population studied. Overweight/obese subjects had a 9.68 increased odds of (95% CI: 6.65–14.09) the MetS compared to their normal-weight counterparts. For all the three risk factors, AUC ranged between 0.84 and 0.88, 0.83 and 0.87, and 0.86 and 0.89 in waist circumference, abdominal obesity, and BMI for boys and between 0.78 and 0.97, 0.67 and 0.93, and 0.82 and 0.96 for girls, respectively. Conclusion. The findings from this study provide alarming evidence-based data on the considerable prevalence of obesity, MetS, and CVD risk factors in the adolescent age group. These results are confirmatory evidence for the necessity of primordial/primary prevention of noncommunicable disease should be considered as a health priority in communities facing a double burden of nutritional disorders

A Study on the Possibility of Extraction, Identification and Removal of Metallic Ions and Resins in Bleached Bagass Pulp by ECF Stages

This study and it aim was accomplished on extraction, identification and removal of metallic ions and resins in bleached Bagass pulp by ECF stages. For this purpose, the samples were randomly selected from soda unbleached pulp of Pars mill. The first, pulp flour provided and measured ash and extractive percent by the TAPPI standards. Then the mineral compounds dissolved in 65% nitric acid and the mineral compounds of pulps identificated by Atomic adsorption method. The results of this study showed that the most of Pb, Cu, Zn ions were in H2O2 bleached pulp, the most of Ni ion in OD(Ep)P stage and the most of Fe ion in unbleached soda pulp. The results showed that OD(Ep)D stage decrease Fe ion and EH stage decrease Ni, Cu and Zn ions in pulps. The results of GC-MS diagram showed that 35 compounds were identified in unbleached soda pulp after cooking, that 1,2-Benzendicarboxylic acid, Hexadecanoic acid, Hexadecan, Octadecan, p-Xylene, 4-Hydroxy-4-Methyl-2-Pantanone,Ethylbenzen and Dodecan were important chemical components in samples. In generally, 17,12 and 14 compounds were identified in EH, OD(Ep)P and OD(Ep)D bleaching stages, respectively, so that, 1,2-Benzendicarboxylic acid and p-Xylene were 2 common and important chemical components in all samples and Octadecan and 9-Dodecanoic acid remove as two components from pulps after bleaching, too. The results showed that EH, OD(Ep)P and OD(Ep)D bleaching stages were very important in bleaching, removal of chemical components and brightness stability of soda pulps

The Effect of ECF Bleaching on Optical and Mechanical Properties of Bagasse Soda Pulp

This study and it aim was accomplished on the effect of ECF bleaching on optical and mechanical properties of bagasse soda pulp. For this purpose, some soda unbleached pulps were randomly selected from Pars mill. Then these pulps were bleached with OD(Ep)P and OD(Ep)D stages by oxygen, chlorine dioxide and hydrogen peroxide. The hand sheets with 60 gr/m2 were prepared from soda unbleached and bleached from EH(control), OD(Ep)P and OD(Ep)D stages pulps, then the optical and mechanical properties were measured and compared according by using TAPPI Standard test methods. The results showed that residual lignin and kappa number decreased following above-mentioned of bleaching stages. The brightness, greenness and K/S ratio were increased in bagasse soda pulps by bleaching. In different treatments, the brightness, opacity, absorbance coefficient, burst, breaking length, tensile, tear strength sensible increased in OD(Ep)P and OD(Ep)D as compared with EH (control) mill bleaching stages. In general, first OD(Ep)P stage, then OD(Ep)D were better than EH (control) mill stage in more of were measured properties

Input correlations impede suppression of chaos and learning in balanced firing-rate networks.

Neural circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control spontaneous network activity. We show that in firing-rate networks in the balanced state, external control of recurrent dynamics, i.e., the suppression of internally-generated chaotic variability, strongly depends on correlations in the input. A distinctive feature of balanced networks is that, because common external input is dynamically canceled by recurrent feedback, it is far more difficult to suppress chaos with common input into each neuron than through independent input. To study this phenomenon, we develop a non-stationary dynamic mean-field theory for driven networks. The theory explains how the activity statistics and the largest Lyapunov exponent depend on the frequency and amplitude of the input, recurrent coupling strength, and network size, for both common and independent input. We further show that uncorrelated inputs facilitate learning in balanced networks

No qualitative difference in chaos suppression by common vs independent input in canonical random networks.

A) as a function of input frequency f ( light color, g = 2 dark color). has a minimum for both common and independent input. The independent input case is identical to the scenario studied in [3]. At high f, the low-pass filter effect of the leak term attenuates the external input for both cases, thus resulting in a linearly increasing . B) Dependence of on the gain parameter g for both low input frequency (f = 0.01/τ, dark color) and high input frequency (f = 0.2/τ, light color), showing a monotonic increase. Error bars indicate ±2 std. Model parameters: N = 5000, }, f ∈ {0.01, 0.2}/τ, I0 = J0 = 0.</p

Activity, population firing rate and autocorrelations of balanced networks with common input.

A) Firing rates ϕi(t) = ϕ(hi(t)) of three example units. B) Mean population firing rate ν(t). C) Time-averaged two-time autocorrelation function (Eq 5) as a function of time difference with no external input (I1 = 0). D-F) Same as A-C but for input amplitude ; activity remains chaotic. G-I Same as A-C but for stronger input (); activity is entrained by the external input and is no longer chaotic. Dashed lines (middle and right columns) are results of non-stationary DMFT, full lines are median across 10 network realizations. Model parameters: N = 5000, g = 2, f = 0.05/τ, I0 = J0 = 1.</p

Dynamic mean-field theory captures frequency-dependent effects on the suppression of chaos.

A) as a function of input frequency f (g = 1.6 light color, g = 2 dark color). has a minimum that is captured by the non-stationary DMFT (dashed green line) but not by the quasi-static approximation (dotted green line), which does not depend on frequency f. At high f, the low-pass filter effect of the leak term attenuates the external input modulation for both cases, thus resulting in a linearly increasing . B) Dependence of on the gain parameter g for high input frequency (f = 0.2/τ), showing a monotonic increase. The non-stationary DMFT results are in good agreement with numerical simulations. For comparison, we include the result of the quasi-static approximation (dotted green line), which shows a more gradual dependence on g and applies only at low frequencies (see Fig 3). Error bars indicate ±2 std. Model parameters: N = 5000, g = 2, f = 0.2/τ, I0 = J0 = 1.</p

Common input impedes learning in balanced networks.

A) Schematic of the training setup. A ‘student network’ (S) is trained to autonomously generate the output , by matching its recurrent inputs to those of a driven ‘teacher network’, whose weights are not changed during training. B) λ1 in the teacher network as a function of I1. C) Test error in the student network as a function of I1. Critical input amplitude is indicated by vertical dashed lines. Consistent with the difference in , the teacher networks driven with common input require a larger I1 to achieve small test errors in the student network. Error bars indicate interquartile range around the median. D) Top: Target output (green) and actual output z (dashed orange) for two input amplitudes I1 ∈ {5, 15}. Bottom: Firing rate ϕ(hi) for two example neurons in teacher network with common input (green full line) and student network (orange dotted line) for two input amplitudes. E) Scatter plot of test error as a function of λ1 for each network realization in A and B, with both common and independent input. When chaos in the teacher network is not suppressed (λ1 > 0), test error is high. Training is successful (small test error) when targets are strong enough to suppress chaos in the teacher network. Training is terminated when error reaches below 10−2. Model parameters: N = 500, g = 2, I0 = J0 = 1, ϕ(x) = max(x, 0) in both teacher and student networks; f = 0.2/τ in the teacher network inputs and target .</p

Largest Lyapunov exponent shows different chaos suppression for common vs independent input.

Largest Lyapunov exponent λ1 as a function of input modulation amplitude I1 for common (green) and independent (violet) input. are the zero-crossings of λ1 and thus the minimum I1 required to suppress chaotic dynamics. With common input, λ1 crosses zero at a much larger I1. Dots with error bars are numerical simulations, dashed lines are largest Lyapunov exponents computed by dynamic mean-field theory (DMFT). Error bars indicate ±2 std across 10 network realizations. Model parameters: N = 5000, g = 2, f = 0.2/τ, I0 = J0 = 1.</p